Download 4. Two sample classes encoded: motion verbs and `know verbs`

Definition of the links
and subsets for verbs
Final Version 6
November, 7, 1996
Antonietta Alonge*
Istituto di Linguistica - Università di Perugia
Istituto di Linguistica Computazionale del C.N.R., Pisa
Deliverable D006, WP4.1, EuroWordNet, LE2-4003
* This document compiles work from: the AMS group, in relation to Dutch; the PSA group, in relation to Italian; the FUE
group, in relation to Spanish. People from all the groups have somehow contributed to it, but, in particular, Piek Vossen,
Nicoletta Calzolari, and Simonetta Montemagni have provided significant comments and contributions to its content.
Identification number
LE-4003-D-006
Type
Document
Title
Definition of the links and subsets for verbs
Status
Final
Deliverable
D006
Work Package
WP4
Task
T4.1
Period covered
June-August1996
Date
07/11/96
Version
6
Number of pages
188
Authors
Antonietta Alonge
WP/Task responsible
Project contact point
EC project officer
Status
PSA
Piek Vossen
Computer Centrum Letteren
University of Amsterdam
Spuistraat 134
1012 VB Amsterdam
The Netherlands
tel. +31 20 525 4624
fax. +31 20 525 4429
e-mail: [email protected]
Jose Soler
Public
Actual distribution
Project Consortium
Suplementary notes
n.a.
Key words
Semantic databases, Information Retrieval, Language Engineering,
Semantic Relations, Semantic Test Sentences
Abstract
This deliverable reports on the work carried out within Task 4.1,
assigned to WP4. The objectives of this task were i) the definition of
the relations to be encoded for verbs; ii) the identification of criteria
for verification of such relations for each language; iii) the definition
of the subsets of verbs for which relations will be encoded in the
database. The document also reports on the experiments carried out
ons specific semantic domains in order to foresee the main issues
and problems to be dealt with when building the whole
EuroWordNet database for verbs.
Complete
Status of the abstract
Received on
Recipient’s catalogue
number
Executive Summary
The goal of this deliverable is to report on the work carried out within Task 4.1, assigned to WP4. The
objectives of this task were i) the definition of the links for verbs; ii) the identification of criteria for
verification of such links for each language; iii) the definition of the subsets of verbs for which
relations will be encoded in the database. Of course, the results obtained so far with respect to these
three issues should be considered, within reasonable limits, to be 'preliminary', since further work on
data might produce some (minor) changes either as far as the links, and therefore the criteria to
establish them, or the verbs selected are concerned.
The document is organised as follows. Section 1. provides a brief introduction to the general goals of
the project and to the specific goals of this Task. In section 2. we provide discussion and definitions
both of the basic relations, already encoded with respect to verbs in WN 1.5 and also used to develop
our resource, and of the new relations identified in order to code all the semantic information which we
may acquire from MRDs, but also from other sources of lexical data, and which can be useful for both
IRSs or other LE applications.
As far as the basic relations already used in WN 1.5 are concerned, we have provided the possibility to
add labels to them indicating disjunction, conjunction, factitivity and negation. On the other hand, the
new relations added, linking verbs to words in other lexical classes, allow the encoding of more subtle
semantic distinctions, thus giving us the possibility both to encode the kind of data which are typically
found in MRDs (which are our main source of data), and to meet the user-requirements. However,
since these 'new' relations require more complicated procedures in order to be acquired, for the time
being their values will be coded only in relation to those subsets of verbs for which we have enough
data. Indeed, we aimed at designing a 'model' general enough to incorporate the different types of
semantic relations that are both extractable from dictionaries (and other sources) and of some usage for
LE applications. The definition of such a broad model does not imply that we fill the whole of it. In
fact, we will encode all the relations already indicated as being fundamental for our database within the
TA, while the others will be filled more as an exemplification of their statute, their extractability, etc.
In section 2., clear criteria for the identification of each relation are also provided, both as general
criteria which define a relation, and as language-specific criteria which allow the identification of the
relations for each language. Although we briefly refer to extraction issues, the extraction procedures for
each wordnet will be described in detail in the deliverables D021 and D025.
In section 3. we discuss issues connected with the definition of the subsets of verbs which will be
encoded in our database, explaining in particular the methodology followed in order to identify a first
subset of common Base Concepts. In section 4. we discuss the first results obtained by trying to code
data on the relations chosen with respect to two 'sample' classes of verbs: motion and know verbs.
Finally, the Appendix provides: i) the specific tests elaborated for each language in order to identify
relations; ii) detailed data related to the Base Concepts identified so far for each language; iii) detailed
data on the results of the 'experiments' on coding information on relations for both motion and know
verbs in each language.
Table of contents
1 Introduction ...........................................................................................................................................7
2 Identifying the prominent semantic relations and correlated links .....................................................10
2.1 Verb-to-verb links and verb-to-x links .........................................................................................15
2.2 Synonymy and synsets .................................................................................................................17
2.2.1 Detecting Synonymy between verbs ......................................................................................19
2.2.2 NEAR_SYNONYMY ............................................................................................................19
2.2.3 XPOS_NEAR_SYNONYMY................................................................................................20
2.3 Hyperonymy and Hyponymy .......................................................................................................22
2.3.1 HAS_HYPERONYM and HAS_HYPONYM ......................................................................23
2.3.2 HAS_XPOS_HYPERONYM and HAS_XPOS_HYPONYM ..............................................24
2.4 Antonymy .....................................................................................................................................25
2.4.1 ANTONYMY ........................................................................................................................27
2.4.2 NEAR_ANTONYMY............................................................................................................28
2.4.3 XPOS_NEAR_ANTONYMY ...............................................................................................30
2.5 Involved ........................................................................................................................................31
2.6 ‘Causes’ and ‘Is_Caused_By’ ......................................................................................................36
2.7 Has_Subevent and Is_Subevent_Of .............................................................................................43
2.8 Be_In_State and State_Of ............................................................................................................45
3. The definition of the verb subsets ......................................................................................................46
3.1 On selecting ‘Base Concepts’ .......................................................................................................46
3.1.1 Selection of the Dutch subset .................................................................................................47
3.1.2 Selection of the Italian subset ...................................................................................................56
3.1.3 Selection of the Spanish subset ..............................................................................................58
3.1.4 First experiment on verifying intersection among the subsets ...............................................59
3.2 On extending the subset................................................................................................................59
4. Two sample classes encoded: motion verbs and ‘know verbs’ .........................................................61
4.1 On coding the relations for two sample classes of verbs ..............................................................61
4.2 First results of work on Dutch ......................................................................................................62
4.2.1. Motion Verbs...........................................................................................................................63
4.2.1.1 SYNONYMY.....................................................................................................................65
4.2.1.2 HYPONYMY ..................................................................................................................66
4.2.1.3 ANTONYMY ....................................................................................................................66
4.2.1.4 ASSOCIATIVE ...............................................................................................................67
4.2.1.5 REFERENCE...................................................................................................................68
4.2.1.6 CAUSATIVE .....................................................................................................................69
4.2.1.7 INCHOATIVE .................................................................................................................70
4.2.1.8 VERB .................................................................................................................................70
4.2.1.9 PREFERENCE...................................................................................................................71
4.2.1.10. Information in definitions ...............................................................................................72
4.2.2. Verbs of knowing ....................................................................................................................81
4.2.2.1 SYNONYMY.....................................................................................................................83
4.2.2.2 HYPERONYMY ...............................................................................................................83
4.2.2.3. HYPONYMY .................................................................................................................83
4.2.2.4 ANTONYMY ....................................................................................................................84
4.2.2.5 ASSOCIATIVE .................................................................................................................84
4.2.2.6 REFERENCE...................................................................................................................85
4.2.2.7 CAUSATIVE .....................................................................................................................85
4.2.2.8 INCHOATIVE .................................................................................................................85
4.2.2.9 VERB and PREFERENCE ................................................................................................85
4.2.2.10. Information in definitions ...............................................................................................85
4.2.3 Conclusion ................................................................................................................................93
4.3 First Results of work on Italian ....................................................................................................95
4.3.1 Motion Verbs............................................................................................................................95
4.2.2 Know verbs ..........................................................................................................................100
4.4 First results of work on Spanish .................................................................................................100
4.4.1 Subset selection of motion verbs and ‘know verbs’ ...............................................................101
4.4.2. Extraction of relations ...........................................................................................................101
5. Appendix ............................................................................................................................................104
5.1 List of tests for identifying the relations in each language .........................................................104
5.1.2 Tests for Dutch .....................................................................................................................104
5.1.2 Tests for Italian ....................................................................................................................128
5.1.3 Tests for Spanish ..................................................................................................................135
5.2 Data obtained by the analysis performed on Dutch....................................................................142
5.2.1 List of Base Concepts...........................................................................................................142
5.3 Data obtained by the analysis performed on Italian ...................................................................159
5.3.1 List of Base Concepts...........................................................................................................159
5.3.2 Motion verb synsets and taxonomy......................................................................................162
5.3.3 Know verb synsets and taxonomies .....................................................................................165
5.4 Data obtained by the analysis performed on Spanish .................................................................167
5.4.1 List of Base Concepts...........................................................................................................167
5.4.2 Motion verb synsets and taxonomies ...................................................................................170
5.4.3 Know verb synsets and taxonomies .....................................................................................183
References ..............................................................................................................................................184
EuroWordNet D006: Definition of the links and subsets for verbs
1 Introduction
In recent years a trend has emerged, both in theoretical and computational linguistics,
towards integrating syntactic analyses with studies on the semantic features of words. This trend
evidences a radical change of perspective in linguistics, according to which the lexicon has been
assigned a new, central role in the organization of language. Many researchers now agree with the
claim that the syntactic properties of words are ‘determined’ by (or somehow directly connected
with) their semantic features, and many studies are being devoted to the identification of classes of
words sharing semantic/syntactic characteristics and, more in general, to lexical semantics. Such
studies present a direct interest for computational applications, since identifying lexical classes can
be of great help to the task of building lexicons for Natural Language Processing systems (cf., e.g.,
papers describing the work carried out within the Esprit-Acquilex project).
If the research carried out so far has demonstrated the necessity of linking syntax to
semantics and, therefore, of analysing the semantic features of words, further work in the field of
computational linguistics is emphasizing even more clearly the importance of developing semantic
databases. It is now clear that Language Engineering applications can gain great advantages from
the possibility of using resources containing semantic data on words.
The main goal of the EuroWordNet project, in fact, is building a (multilingual) semantic
database to be used as a tool for Information Retrieval Systems (but also for other LE applications),
given that access to information stored electronically is still limited by key word matching or fixed
indexing and classification systems. To be able to retrieve information by using semantic data,
instead of matching key words, may multiply the possibility of identifying the relevant electronic
text resources; moreover, the availability of a multilingual tool, by means of which whole sets of
synonyms (‘synsets’) in different languages are connected on the basis of their semantic properties,
may allow easy accessing of information stored in various languages (cf. Deliverable D001).
The goal of this deliverable is to report on the work carried out within Task 4.1, assigned to
WP4. The objectives of this task were i) the definition of the links for verbs; ii) the identification of
criteria for verification of such links for each language; iii) the definition of the subsets of verbs for
which relations will be encoded in the database. Of course, the results obtained so far with respect
to these three issues should be considered, within reasonable limits, to be ‘preliminary’, since
further work on data might produce some (minor) changes either as far as the links, and therefore
the criteria to establish them, or the verbs to be selected are concerned.1
Within WordNet 1.5 (henceforth WN 1.5) English nouns, verbs and adjectives are organized
into synonym sets, each representing one underlying lexical concept. Each ‘synset’ is then linked to
others by means of a number of relations: hyperonymy, hyponymy, antonymy, meronymy and
holonymy (for nouns), coordinate words (i.e., words which have the same hypernym, indicated only
for nouns), causation and entailment (for verbs) (cf. Miller et al. 1993). Such relations already
provide useful semantic information on words, however they do not seem sufficient to describe all
1
The results obtained in the various stages of our research can be found at the WWW-site of the project (http:
//www.let.uva.nl/CCL/EuroWordNet.html). Thus, at the moment, the data available are the following: 1) first list of
base concepts; 2) first list of relations; 3) first definition of relations with tests.
LE2-4003
EuroWordNet
7
EuroWordNet D006: Definition of the links and subsets for verbs
the meaning nuances which could instead be useful for IRS and other LE applications, especially
because within MRDs, which are our main sources of data, information is stored in such a way that
data coded by means of these relations are only a part of all the available and necessary data. For
this reason, in EuroWordNet we have distinguished additional relations, even if, since they require
more complicated procedures in order to be extracted from existing repositories of data, for the time
being their values will be coded only in relation to those subsets of words for which we already
have enough data. Indeed, we aim at designing a ‘model’ general enough to incorporate the
different types of semantic relations that are both extractable from dictionaries (and other sources)
and of some usage for LE applications. The definition of such a broad model does not imply that we
fill the whole of it. In fact, we will encode all the relations already indicated as being fundamental
for our database within the TA, while the others will be filled more as an exemplification of their
statute, their extractability, etc.
In general, the definition of the relations had to meet the following requirements:
• to be possible, given the resources used, to extract the relations identified;
• to provide a common framework to achieve coherence during building across the sites;
• to make it possible to implement consistency checking mechanisms in the multilingual
database;
• to make it possible for external reviewers to verify the quality of the results obtained.
In order to explore the effectiveness of different approaches, methodologies of analysis and
solutions for encoding data, but also for arriving both at a final definitions of the relations to be
included in our database and at a definition of clear criteria for identifying them in each language,
each site performed an ‘experiment’, by taking into account two small semantic fields: i) the
taxonomy built starting from the hyperonym verb(s) corresponding to WN 1.5 {move, change
position} synset;2 ii) ‘know’ verbs. The purpose of the experiment, whose results will be reported
in the deliverable, was, therefore, twofold:
• to determine the best approach and methodologies of analysis for the different resources used
by each group;
• to explicitly define the relations and the criteria to encode them for each language.
As recalled above, another basic issue to be dealt with within Task 4.1 was the definition of
the subsets of verbs for which the relations will be encoded in the database. Thus, first of all we
have carried out a work aimed at identifying a set of common ‘Base Concepts’ for each language
wordnet, by taking into consideration the defining vocabulary of each dictionary. The construction
of such initial subset was important to set up a common framework to build the individual
wordnets. The aim was to create a core subset for each language that contains the most relevant
synsets (according to the number of relations per synset and their position in the hierarchy) and is
highly overlapped with the core subsets of the other languages. Secondly, criteria have been clearly
defined to extend this first subset in order to obtain the about 15,000 verb synsets to be encoded for
each wordnet, as indicated within the TA. The methodology identified should guarantee that each
language wordnet will be maximally balanced and compatible with the others, in order to allow a
real multilingual usage of the database.
The document is organised as follows. In section 2. we provide a brief discussion and
definition of the relations already encoded with respect to verbs in WN 1.5 and also used to develop
2
Here and later, the curly brackets are used to indicate a synset.
LE2-4003
EuroWordNet
8
EuroWordNet D006: Definition of the links and subsets for verbs
our resource, and of the new relations added in order to code all the information which we may
acquire from MRDs, but also from other sources of lexical data, and which can be useful for both
IRSs and other LE applications. Clear criteria for the identification of each relation are also
provided. Although we shall briefly refer to extraction issues, the extraction procedures for each
wordnet will be described in detail in the deliverables D021 and D025. In section 3. we discuss
issues connected with the definition of the subsets of verbs which will be encoded in our database,
explaining in particular the methodology followed in order to identify a first subset of common
Base Concepts. In section 4. we discuss the first results obtained by trying to code data on the
relations chosen with respect to the two ‘sample’ classes of verbs: motion and know verbs. Finally,
in the Appendix we provide: i) the specific tests elaborated for each language in order to identify
relations; ii) detailed data related to the Base Concepts identified so far for each language; iii)
detailed data on the results of the ‘experiments’ on coding information on relations for both motion
and know verbs.
LE2-4003
EuroWordNet
9
10
EuroWordNet D006: Definition of the links and subsets for verbs
2 Identifying the prominent semantic relations and correlated
links
Within this deliverable we are concerned with the ‘Language Internal Relations’ (cf. D007 on
the “High-Level Architecture of the EuroWordNet Database”) and the kind of criteria defined to
identify them. We shall, however, try to limit ourselves to considerations which are particularly
relevant with respect to the verb relations, since a discussion of the relations concerning nouns is
provided in D005 (on the “Definition of the links and subsets for nouns”), together with a more general
introduction to the characteristics of the relations and the database. Notwithstanding this, for clearness
of exposition, in some cases it will be necessary to repeat information somehow already provided in the
Deliverable on nouns.
The definition of the final list of relations required a quite long period of discussion and
elaboration by the partners of the project. Here below, the complete list of the relations on which we
have agreed upon is reported for a quick reference, with the following information for each relation: i)
its name, ii) the parts of speech linked (with the indication of the ‘direction’ of the linking), iii) further
labels which eventually apply to the relation itself, iv) the type of data linked (i.e., Word Meanings or
synset Variants).
Relation Type
Parts of Speech
Labels
Data
Types
NEAR_SYNONYM
XPOS_NEAR_SYNONYM
N<>N, V<>V
N<>V, N<>AdjAdv, V<>AdjAdv
reversed
reversed
WM<>WM
WM<>WM
HAS_HYPERONYM
HAS_HYPONYM
HAS_XPOS_HYPERONYM
N>N, V>V
N>N, V>V
N>V,
N>AdjAdv, V>AdjAdv,
V>N, AdjAdv>N, AdjAdv>V
N>V,
N>AdjAdv, V>AdjAdv,
V>N, AdjAdv>N, AdjAdv>V
Dis, Con, reversed
Dis, reversed
Dis, Con, reversed
WM<>WM
WM<>WM
WM<>WM
Dis, reversed
WM<>WM
N>N
N>N
N>N
N>N
N>N
N>N
N>N
N>N
N>N
N>N
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
Dis, Con, reversed, not
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
HAS_XPOS_HYPONYM
HAS_HOLONYM
HAS_HOLO_PART
HAS_HOLO_MEMBER
HAS_HOLO_PORTION
HAS_HOLO_MADEOF
HAS_HOLO_LOCATION
HAS_MERONYM
HAS_MERO_PART
HAS_MERO_MEMBER
HAS_MERO_MADEOF
LE2-4003
EuroWordNet
11
EuroWordNet D006: Definition of the links and subsets for verbs
HAS_MERO_LOCATION
N>N
Dis, Con, reversed, not
WM<>WM
ANTONYM
NEAR_ANTONYM
XPOS_NEAR_ANTONYM
N<>N, V<>V
N<>N, V<>V
N<>V, N<>AdjAdv, V<>AdjAdv
reversed
reversed
reversed
VA<>VA
WM<>WM
WM<>WM
CAUSES
V>V, N>V, N>N, V>N, V>AdjAdv, Dis,
Con,
N>AdjAdv
reversed, not
V>V, N>V, N>N, V>N, AdjAdv>V, Dis,
Con,
AdjAdv>N
reversed, not
IS_CAUSED_BY
Factive, WM<>WM
Factive, WM<>WM
HAS_SUBEVENT
IS_SUBEVENT_OF
V>V, N>V, N>N, V>N
V>V, N>V, N>N, V>N
Dis, Con, reversed, not
Dis, Con, reversed, not
WM<>WM
WM<>WM
ROLE
ROLE_AGENT
ROLE_INSTRUMENT
ROLE_PATIENT
ROLE_LOCATION
ROLE_DIRECTION
ROLE_SOURCE_ DIRECTION
ROLE_TARGET_ DIRECTION
INVOLVED
INVOLVED_AGENT
INVOLVED_PATIENT
INVOLVED_INSTRUMENT
INVOLVED_LOCATION
INVOLVED_DIRECTION
INVOLVED_SOURCE_DIRECTION
INVOLVED_TARGET_DIRECTION
N>V, N>N
N>V, N>N
N>V, N>N
N>V, N>N
N>V, N>N, AdjAdv>N, AdjAdv>V
N>V, N>N, AdjAdv>N, AdjAdv>V
N>V, N>N, AdjAdv>N, AdjAdv>V
N>V, N>N, AdjAdv>N, AdjAdv>V
V>N, N>N
V>N, N>N
V>N, N>N
V>N, N>N
V>N, N>N, N>AdjAdv, V>AdjAdv
V>N, N>N, N>AdjAdv, V>AdjAdv
V>N, N>N, N>AdjAdv, V>AdjAdv
V>N, N>N, N>AdjAdv, V>AdjAdv
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
Dis, Con, reversed
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
WM<>WM
BE_IN_STATE
STATE_OF
N>AdjAdv, V>AdjAdv
AdjAdv>N, AdjAdv>V
Dis, Con, reversed, not
Dis, Con, reversed, not
WM<>WM
WM<>WM
FUZZYNYM
XPOS_FUZZYNYM
N<>N, V<>V
N<>V, V<>AdjAdv, N<>AdjAdv
WM<>WM
WM<>WM
EQ_SYNONYM
EQ_NEAR_SYNONYM
N<>N, V<>V
N<>N,
V<>V,
V<>AdjAdv,
WM<>ILIR
WM<>ILIR
HAS_EQ_HYPERONYM
N>N, N>V, N>AdjAdv, V>V, V>N,
V>AdjAdv, AdjAdv>N, AdjAdv>V
N>N, N>V, N>AdjAdv, V>V, V>N,
V>AdjAdv, AdjAdv>N, AdjAdv>V
N>N
N>N
HAS_EQ_HYPONYM
HAS_EQ_HOLONYM
HAS_EQ_MERONYM
LE2-4003
N<>AdjAdv,
WM<>ILIR
WM<>ILIR
WM<>ILIR
WM<>ILIR
WM<>ILIR
EuroWordNet
12
EuroWordNet D006: Definition of the links and subsets for verbs
EQ_INVOLVED
EQ_ROLE
N>N, V>N
N>N, N>V
EQ_CAUSES
N>N, V>V, N>V, V>N, V>AdjAdv,
N>AdjAdv
N>N, V>V, N>V, V>N, AdjAdv>V,
AdjAdvV>N
EQ_IS_CAUSED_BY
WM<>ILIR
WM<>ILIR
WM<>ILIR
WM<>ILIR
WM<>ILIR
EQ_HAS_SUBEVENT
EQ_IS_SUBEVENT_OF
N>N, V>V, N>V, V>N
N>N, V>V, N>V, V>N
WM<>ILIR
WM<>ILIR
EQ_BE_IN_STATE
EQ_IS_STATE_OF
V>AdjAdv, N>AdjAdv
AdjAdv>V, AdjAdv>N,
WM<>ILIR
WM<>ILIR
HAS_INSTANCE
BELONGS_TO_CLASS
N>PN
PN>N
WM>I
I>WM
Parts of Speech:
N = noun
V = verb
AdjAdv = Adjective or Adverb
PN = pronoun or name
Labels:
factive/non-factive = restricted to Lyon’s distinction between factive causes and modally more complex causes.
reversed = for relations which are not explicitly stated but generated by automatic reversal.
Dis/Con = for explicitly coded disjunction/conjunction of relations.
not = for cases in which the negative implication holds between word pairs.
Data types:
WM = word meaning or synset
I = instance
ILIR = ILI record
VA = synset variant
Thus, for instance, if we take into consideration the ‘CAUSES’ relation we see that it may link:
i) a verb (synset)3 to another verb; ii) a noun to a verb; iii) a noun to a noun; iv) a verb to a noun. Then,
it may be further labeled, under certain circumstances. The labels ‘Con’ and ‘Dis’ respectively indicate
the possibility of the relation forming conjunctive or disjunctive sets (cf. D001): for instance, to give
causes both to receive and to have, whose synsets will be conjunct, whereas to race may cause either
to win or to lose and these verb synsets will then be disjunct. The label ‘Factive’ is instead used to
indicate that the relation necessarily holds. In fact, following Lyons (1977), different types of causality
can be distinguished, reflecting the factivity of the effect:
• factive: event E1 implies the causation of E2: e.g. “to kill causes to die”;
3
From now on, if not explicitly otherwise indicated, the words verb or noun will be used to indicate also the synsets
containing them.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
13
• non-factive: E1 probably or likely causes event E2 or E1 is intended to cause some event E2: “to
search may cause to find”.
The label ‘reversed’, then, is used when a relation has been automatically stated by reversing its
counter-part relation. Indeed, it is a requirement of the database that every relation has a reverse
counter-part. However, there is a difference between relations which are explicitly coded as reverse
relations and relations which are automatically reversed because of this requirement. For example, to
chain causes to be chained and, on the other hand, to be chained is necessarily caused by to chain, i.e.
in this case we have a conceptually bi-directional link. In the case of the pair kill/die, instead, the fact
that to die will result as being ‘CAUSED_BY’ to kill is due to the reversing of the factive-causation
relation linking to kill to to die: thus, by labelling the relation as ‘reversed’ we imply that to die is not
necessarily caused by to kill. In other words, to be able to distinguish between the
conceptually-dependent and the automatically reversed relations we shall use the label ‘reversed’.
Finally, there is the possibility to have a negation label ‘not’ to explicitly express that a relation does
not hold. Such a negative relation is not necessarily the same as antonymy, which expresses opposition.
For instance, to ie can in no case cause to move.
As said above, here we shall take into consideration only the LIRs, assuming that interlingual
relations (between each language and WN 1.5, which is used as an interlingual connection) are
equivalent. Thus, for instance, SYNONYM and EQ_SYNONYM can be defined in a similar way, even
if the latter holds between words of different languages.
In principle, the relations are incompatible: i.e., if two verbs are linked by a relation they cannot
be linked by means of any other relation. Some exceptions to this general rule have, however, been
found. For instance, sometimes it can be difficult to distinguish between hyponymy and cause relation:
this happens for the Dutch verb dichttrekken (to pull in order to close), which could be described both
as a hyponym and as a cause of the verb sluiten (to close). In this case, it is very difficult to keep the
two relations apart because the events referred to by the verbs are not disjoint and, in fact, it is also
difficult to state if these events are really different (in any case, the decision has been taken to encode
this, and similar cases, as hyponymy relations). Moreover, also the HAS_SUBEVENT relation may in
some cases display a kind of overlap with other relations. For instance, to buy has been described as
connected to the verb to pay by this relation (i.e., pay IS_SUBEVENT_OF buy - non-factive - / buy
HAS_SUBEVENT pay), but since this can be seen as a sort of ‘class-inclusion’ relation, we could also
state that the two verbs are related by hyperonymy-hyponymy.4
In order to develop criteria for verifying the existence of the semantic relations we tried to
identify substition tests or diagnostic frames based on normality judgements (cf. Cruse 1986). For
every relation, thus, some general tests will be provided containing different frames; by inserting two
words, different ‘normality’/‘abnormality’ judgements will be determined (generally indicated by
means of a ‘yes/no’ score, but, in some cases, also other values will be allowed: probably, unclear,
4
Cf. D005 for a similar discussion with respect to compatibility of hyperonymy/hyponymy and set-membership of nouns.
Indeed, the HAS_SUBEVENT relation functions as a kind of ‘meronymy relation’ for verbs.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
14
unlikely), on the basis of which a relation can be detected. These tests are semantic and not intended to
cover syntactic differences, differences in register, style or dialect nor pragmatic differences between
words. Since there are cases in which it is difficult to assign clear scores to the tests, or the results are
all negative, but the test X has some strong relation to Y still works, we have created a relation called
‘FUZZYNYM’. A fuzzynym relation can hold between same part-of-speech words or across parts of
speech.
In the following, while defining the specific relations being encoded for verbs and providing
general formal criteria for identifying them, we shall discuss in more details, when necessary, issues
connected with the status of each relation and with the labels which eventually apply to it. In the
Appendix also more specific tests for each language will be provided.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
15
2.1 Verb-to-verb links and verb-to-x links
Within WN 1.5 all the relations encoded apply among words of the same lexical class.
However, both theoretical and practical reasons led us to feel the necessity of adding to our database
also semantic relations applying to elements of different lexical classes.
Indeed, one of the issues discussed within D001 was the necessity of interlinking the noun and
verb networks. Actually, the Princeton WordNet uses a rigid distinction between nouns and verbs,
mainly because of their different syntactic role in English. Furthermore, it is suggested that the
responses of people to interviews parallel the rigid noun/verb distinction as well: nouns are related to
nouns and verbs are related to verbs. As shown in D001 (p. 34), this “separation however leads to a
very undesirable situation that very similar synsets (which in some cases even have exactly the same
names for the tops) are totally unrelated only because they differ in part of speech”. Thus, rather then
semantically distinguishing nouns and verbs, the decision was taken to distinguish between first-order
and higher-order entities, where nouns can refer to first-order entities (concrete, physical things) and
both nouns and verbs can refer to higher-order entities (properties of things, relations, acts, activities,
processes, states, etc. (cf. D001, p.34). Further reasons motivating this decision were also indicated
(ibid.):
• In other languages it is either very difficult to distinguish nouns and verbs or the distinction does not even exist
(Lyons 1977).
• From an information retrieval point of view the same information can be coded in an NP or in a sentence. By
unifying higher-order nouns and verbs in the same ontology it will be possible to match expressions with very
different syntactic structures but comparable content (see the Sift project, LRE 62030).
• By merging verbs and abstract nouns we can more easily link mismatches across languages that involve a
part-of-speech shift.
Therefore, we have inserted in the architecture of our database synonymy,
hyperonymy/hyponymy and antonymy relations applying across parts of speech. As a consequence of
further developments of our research, besides allowing the linking between noun and verb hierarchies,
these relations can now be used to link also adjectives and adverbs to the noun and verb networks. Of
course, since in the database being developed in this project we are only building noun and verb
networks, we shall not create wordnets for adjectives and adverbs as well, but we shall simply include
the adjectives or adverbs referred to by means of the ‘XPOS’ relations in the lexicon, without creating
any other link for them.
Within the Deliverable D001 it was, then, pointed out that MRDs will be our main sources of
data and that the information there is stored in such a way that the relations used in the Princeton
wordnet are not adequate to code the kind of data that they contain and that are necessary for IRS (and
other LE applications). In fact, the frequency of ‘shallow’ hierarchy structures in dictionaries “suggests
that other relations are more important or useful for classifying verbs” (D001, p. 44) with respect to
those available within WN 1.5. Consider, for instance, Italian dictionaries: there we find many (classes
of) verbs defined by means of a phrase whose syntactic head (the hyperonym) is not very significant
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
16
whereas what follows it (be it a noun, a NP, a PP, etc.) is of fundamental importance in order to obtain
necessary information on the semantics of the verb considered. The following verbs of motion and their
definitions can be taken as examples:
camminare =
to walk
cavalcare =
to ride
“andare a piedi”
to go on foot
“andare a cavallo”
to go on a horse
coricarsi =
“andare a letto”
to go to bed
navigare =
to navigate
“andare (detto di navi)”
to go (said of ships)
uscire =
to go out
“andare, venire fuori”
to go, to come out
All such verbs are hyponyms of the verb andare (to go) which simply indicates motion along a path,
however: the first verb is a manner-of-motion verb, the second one indicates motion by means of a
vehicle, the third one motion to a specific place (incorporated within the meaning of the verb itself), the
fourth one is used to refer to motion performed (only) by specific vehicles, and the last one indicates
motion from and to partially-specified locations (from inside to outside). In order to be able to code
more detailed information on these verb meaning components we should, in principle, take into
consideration any element in their definitions.
A somehow similar situation holds in the case of the diventare/rendere (to become/to make)
hyponyms which cannot be simply connected with their superordinates if one does not want to lose
some relevant information: in fact, verbs like arrossire (“diventare rosso” = to become red) or
imbiancarsi (“diventare bianco” = to become white), intristire (“diventare triste” = to become sad)
and ingrassarsi (“diventare grasso” = to become fat) are all inchoative, as we infer from the fact that
they are all hyponyms of diventare, but they also display semantic differences which may be very
important from the point of view of LE applications: indeed, whereas the former two verbs refer to a
change in colour, the third one refers to a change in mood and the latter to a change in weight. Again, it
seemed necessary to define a model providing a framework which is enriched with respect to the WN
1.5 inventory, so to have links which might connect each entry-verb with all the significant elements
found in its dictionary definition and useful from the point of view of a EuroWordNet user.
Some new relations have therefore been identified, which should cover a number of semantic
relations among words from different parts of speech, in order to give us the possibility to code the data
on the semantics of words (different from those coded by means of already existing relations) which
we are able to acquire in one way or another and which are relevant to delineate the meaning of the
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
17
verb. Indeed, we have assumed a view of the meaning of a word similar to that proposed by Cruse
(1986) (and before by Haas, 1964), according to which the meaning of a word is conceived of as a kind
of ‘semantic field’, containing all possible (grammatical) sentential contexts of the word, and all
possible (grammatical) substitutes within those contexts.5 This means to see the meaning of a word as
being made up, at least in part, of (the meanings of) other words, which correspond, therefore, to
‘semantic traits’ of the first word. Following Cruse, such semantic traits should carry the lightest
possible burden of theory, and in fact no claim is made that they are either primitive, functionally
discrete, universal, or drawn from a finite inventory; furthermore, in Cruse’s view there is no claim that
the meaning of any word can be exhaustively characterised by any finite set of such semantic traits.
Such an ‘enlarged’, even if lacking in strong theoretical assumptions, view of the meaning of a
word may allow us coding a large inventory of semantic data, i.e., different arguments ‘incorporated’
within the verb root, by means of the indication of the actual words generally used to define another
word (mainly within dictionaries but also in other sources). By adopting this view, we can code
information on ‘semantic components’, by relating a word with other words (e. g., a verb with the
words which can be found together with its hyperonym in a context) rather than words with simple
labels for concepts. In other words, we aim at encoding semantic relations among words as they are
lexicalized in a language system. Although this is different from encoding world-knowledge data, the
linguistic database could then be used as a basis for eventually deriving a world-knowledge database.
2.2 Synonymy and synsets
That of synonymy is the fundamental relation on which WN 1.5 is based, since the first
organization of words is realized by means of ‘synsets’, i.e. sets of synonymous words. The notion of
synonymy adopted is not the strongest one, according to which two expressions are synonymous if the
substitution of one for the other never changes the truth value of a sentence in which the substitution is
made, but a weakened version of it, stating that “two expressions are synonymous in a linguistic
context C if the substitution of one for the other in C does not alter the truth value.” (Miller et al. 1993)
The notion of synonymy adopted in our project in order to build synsets is the same as that of
WN 1.5. Synsets are built on the basis of the possibility of a word to be substituted by another in a
specific context, and word (synset) senses are therefore given by all the possible uses of a word in
contexts.
The general test used to state that two verbs are synonymous is the following:
He Vs 1 entails and is entailed by He Vs2
5
This view of the meaning of a word is similar to the ‘relational’ view, adopted, e.g., by Lyons (1968; 1977), according to
whom the meaning of a lexical item can be defined as not only dependent, but also identical to the set of relations which
hold between the item in question and the other lexical items in the system.
LE2-4003
EuroWordNet
18
EuroWordNet D006: Definition of the links and subsets for verbs
Which means that both the following sentences must be true, for two verbs to be synonymous:
- He Vs1 therefore he Vs2
yes
- He Vs2 therefore he Vs1
yes
Clear yes-yes = synonymy
However, such test may be performed only by considering the actual context in which two (or
more) verbs can be found. For instance, if we take into consideration the Italian verb allontanarsi (=
either to move/go away or to go far), we can state that when used in the context (1a) below, it has
certain synonyms, whereas in context (1b) it has others, since the verbs allowed respectively in (1a)
and (1b) are in a relationship of reciprocal entailment in those contexts:
(1a)
(1b)
Giovanni non poteva allontanarsi/muoversi (*andare lontano) da casa.6
Giovanni could not move/go away (*go far) from home.
Giovanni si allontanò troppo/andò troppo lontano (*si mosse troppo) dalla riva e non riuscii più
a vederlo.
Giovanni went too far away (*moved far) from the bank and I could not see him anymore.
To put it in other words, we could state that:
in any sentence S where Verb1 is the head of a VP which is used to identify a situation in discourse
another verb Verb2, which is a synonym of Verb1, can be used as the head of the same VP without
resulting in semantic anomaly
and
in any sentence S where Verb2 is the head of an VP which is used to identify a situation in
discourse another verb Verb1, which is a synonym of Verb2, can be used as the head of the same
VP without resulting in semantic anomaly.
6
As a matter of fact, andare lontano can occur in such a context but with a slightly different meaning with respect to the
synset of allontanarsi/muoversi, i.e. it cannot substitute that synset without altering the meaning of the sentence.
LE2-4003
EuroWordNet
19
EuroWordNet D006: Definition of the links and subsets for verbs
2.2.1 Detecting Synonymy between verbs
In the following, a general formal criterion for the identification of synonymy between verbs is
provided:
Verb test (1)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Synonymy between verbs
Test sentence
If something/someone/it Xs then something/someone/it Ys
iI something/someone/it Ys then something/someone/it Xs
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
If something/someone/it begins then something/someone/it starts
If something/someone/it starts then something/someone/it begins
synset variants: {begin, start}
In practice, the relation of synonymy can be acquired from different information sources, i.e.
from verb entries of both mono- and bi-lingual dictionaries. For instance, as far as Italian is concerned,
explicitly tagged synonyms are extracted from the synonym fields of a monolingual dictionary and
from a synonym dictionary; further information is acquired from synonymical definitions of
monoligual dictionaries and semantic indicators of a bilingual dictionary. The data automatically
extracted from the two latter sources require manual revision and, possibly, correction. Indeed, in these
two cases the synonymy relation is not established on the basis of semantic, cognitive or distributional
criteria, but simply on the basis of ‘external’ criteria such as, for instance, the definition structure: e.g.
whenever a given word is defined by means of a single word (i.e. the definition does not contain either
arguments or modifiers), these two words are taken to be somehow synonymous. Thus, when the verb
returned by the automatic procedure as synonym displays more than one sense, firstly we have to
(manually) identify which is the synonym sense, and then we have to perform tests to verify the
existence of synonymy in a context.
2.2.2 NEAR_SYNONYMY
As we have seen, the basic organization of words within our database will be provided by
synsets, built by using a weakened notion of synonymy. There are, however, cases in which it is very
difficult to state if two words, although displaying a very ‘similar’ meaning, can be put in a same
synset, i.e. are ‘true’ synonyms in the sense explained above. In fact, sometimes the synonym test
between two verbs may yield a not clear score and, more important, the two verbs hyponyms cannot be
interchanged or the hyperonyms are also incompatible, even if these verbs cannot be considered either
connected by a hyponymy relation or by any other relation. The NEAR_SYNONYMY relation can in
general be used for these verb pairs. A general criterion to identify near-synonyms is the following:
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
20
Verb Test (2)
Comment:
Score
yes, probably a
Near-Synonymy between verbs
Test sentence
If something/someone/it Xs then something/someone/it Ys
and
yes/yes
i) Xh and Yh are different or ii) Hx and Hy are different
yes, probably b
If something/someone/it Ys then something/someone/it Xs
and
yes/yes
i) Yh and Xh are different or ii) Hy and Hx are different
Conditions:
- X and Y are verbs in the third person singular
- X and Y are not linked by any other relation
- Xh is the set of the hyponyms (if there are any) of X
- Yh is the set of the hyponyms (if there are any) of Y
- Hx is the set of the hyperonyms (if there are any) of X
- Hy is the set of the hyperonyms (if there are any) of Y
Effect:
X
NEAR_SYNONYM
Y
Y
NEAR_SYNONYM
X
Using the NEAR_SYNONYMY relation we can keep sets of hyponyms separate while we can
still encode that two synsets are close in meaning.
2.2.3 XPOS_NEAR_SYNONYMY
As explained above, and also indicated within the table listing all the relations, some links have
been created to allow synonymy (called in this case ‘near-synonymy’, because of its characteristics)
between different parts of speech. In the following, we provide general criteria for the definition of
XPOS_NEAR_SYNONYMY.
LE2-4003
EuroWordNet
21
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (3)
Comment:
Score
yes
a
yes
b
Conditions:
Example (1):
a
b
Effect:
Example (2):
a
b
Effect:
Verb test (4)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Synonymy between verbs and nouns denoting events or processes
Test sentence
If something/someone/it Xs then a/an Y takes place
If a/an Y takes place then something/someone/it Xs
- X is a verb in the third person singular form
- Y is a noun in the singular
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
If something/someone/it murdersthen a murder takes place
If a murder takes place then something/someone/it murders
to murder (X) XPOS_NEAR_SYNONYM
murder (Y)
murder (Y) XPOS_NEAR_SYNONYM
to murder (X)
If something/someone/it moves then a movement takes place
If a movement takes place then something/someone/it moves
to move (X)
XPOS_NEAR_SYNONYM movement (Y)
movement (Y)
XPOS_NEAR_SYNONYM to move (X)
Synonymy between verbs and nouns denoting states
Test sentence
If something/someone/it Xs then there is a state of Y
If there is a state of Y then something/someone/it Xs
- X is a verb in the third person singular form
- Y is a noun in the singular
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
If something/someone/it exists then there is a state of existence
If there is a state of existence then something/someone/it exists
to exist (X)
XPOS_NEAR_SYNONYM
existence (Y)
existence (Y)
XPOS_NEAR_SYNONYM
to exist (X)
EuroWordNet
22
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (5)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Synonymy between state-denoting verbs and adjectives (or adverbs)7
Test sentence
if something/someone/it Xs then something/someone/it is Y
if something/someone/it is Y then something/someone/it Xs
- X is a verb in the third person singular form
- Y is an adjective
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
if someone/it lives then someone/it is alive
if someone/it is alive then someone/it lives
to live (X)
XPOS_NEAR_SYNONYM
alive (Y)
alive (Y)
XPOS_NEAR_SYNONYM
to live (X)
2.3 Hyperonymy and Hyponymy
The relations of hyperonymy and hyponymy have been largely studied and used in theoretical
and applicative works, and they play a central role also in our tool, since they individuate taxonomies,
i. e. classes of words. Thus, for instance, in order to identify a set of motion verbs we can look for all
the hyponyms of a synset containing top-verbs simply referring to motion. If we want to narrow our
search to verbs referring, for instance, to directed-motion we can look for the hyponyms of a top-verb
synset indicating directed-motion. The advantages of organizing the lexicon into taxonomies are
obvious both for the theoretical field and for applicative uses, the most important one being the
possibility of making inferences.
The general criterion used to state the existence of the relations for verbs is given in the
following (adapted from Cruse 1986):
A verb synset X is a hyponym of another verb synset Y (and, by the same token, Y a
hyperonym of X) if He is X-ing entails but is not entailed by He is Y-ing.
Which means that the following sentences should be respectively true and false, for two verbs be
connected by means of a hyperonymy/hyponymy relation:
- He Vs1 therefore he Vs2
yes
- He Vs2 therefore he Vs1
no
Clear yes-no = V1 is a hyponym of V2 (and V2 is a hyperonym of V1)
7
Since languages tend to differ considerably in what are adjectives and what are adverbs (e.g., in Dutch many adjectives
can be used as adverbs, whereas this is impossible in English), we have decided to allow both categories here and in every
other case of a relation between a verb (or a noun) and an adjective.
LE2-4003
EuroWordNet
23
EuroWordNet D006: Definition of the links and subsets for verbs
2.3.1 HAS_HYPERONYM and HAS_HYPONYM
The general test provided above seems, however, not sufficiently discriminating, because it does not
distinguish between verbs connected by a hyponymy relation and verbs connected by a more general
entailment relation. In fact, in this test, V1 could be, for instance, to snore and V2 could be to sleep
(indeed, He is snoring entails but is not entailed by He is sleeping), which are not connected by a
hyponymy relation. Thus, the test should be reformulated in order to avoid similar wrong results. Since
each hyponym is equivalent to a paraphrase in which its hyperonym is syntagmatically modified, we
can state the following formal criteria for the definition of hyperonymy/hyponymy:
Verb test (6)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Hyperonymy/hyponymy between verb synsets
Test sentence
X is Y + AdvP/AdjP/AdjP/NP/PP
Y is X + AdvP/AdjP/NP/PP
- X is a verb in the infinitive form
- Y is a verb in the infinitive form
- there is at least one specifying AdvP, NP or PP that applies to the Y-phrase
to run is to go fast
* to go is to run fast
{to run} (X)
HAS_HYPERONYM {to go} (Y)
{to go} (Y)
HAS_HYPONYM
{to run} (X)
Hyperonymy/hyponymy relations are typically acquired from dictionary definitions formulated
as complex verbal phrases consisting of a syntactic head and of arguments and/or modifiers. In
definitions with this syntactic structure, the head verb represents the hyperonym of the verb being
defined; conversely, the definiendum is a hyponym of the definition head.8
8
It seems necessary to clarify that although by means of automatic procedures it is ‘easy’ to identify these relations within
dictionaries, such identification does not directly lead to the development of taxonomies, i.e. chains constituted by a top
hyperonym and all its direct and indirect hyponyms. Indeed, as we have already remarked with respect to the identification
of synonymy between word senses, each genus verb may display different senses, one of which has to be identified as the
hyperonym sense. As far as Italian is concerned such sense disambiguation has to be performed ‘manually’.
LE2-4003
EuroWordNet
24
EuroWordNet D006: Definition of the links and subsets for verbs
2.3.2 HAS_XPOS_HYPERONYM and HAS_XPOS_HYPONYM
As already mentioned, a link has been created to allow hyperonymy/hyponymy across parts of
speech. In the following, general criteria for its identification are provided.
Verb test (7)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Verb test (8)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Hyperonymy/Hyponymy between verbs and nouns denoting events and
processes9
Test sentence
If a/an Y takes place then something/someone/it Xs + AdvP/AdjP/NP/PP
If something/someone/it Xs then a certain Y takes place
- X is a verb in the third person singular form
- Y is a noun in the singular
- there is at least one specifying AdvP, NP or PP that applies to the X-phrase
- preferably there is no morphological link between the noun and the verb
If an arrival takes place then someone/it goes to a place
* If someone/it goes then a certain arrival takes place
arrival (Y)
HAS_XPOS_HYPERONYM
to go (X)
to go (X)
HAS_XPOS_HYPONYM
arrival (Y)
Hyperonymy/Hyponymy between verbs and nouns denoting states
Test sentence
If there is a state of Y then something/someone/it Xs + AdvP/AdjP/NP/PP
If something/someone/it Xs then there is a certain state of Y
- X is a verb in the the third person singular form
- Y is a noun in the singular
- there is at least one specifying AdvP, NP or PP that applies to the X-phrase
- preferably there is no morphological link between the noun and the verb
If there is a state of paranoia then someone fears something intensively
* If someone fears something then there is a certain state of paranoia
paranoia (Y) HAS_XPOS_HYPERONYM
to fear (X)
to fear (X)
HAS_XPOS_HYPONYM
paranoia(Y)
9
Note that here and below we provide tests identifying a verb as hyperonym and a noun or adjective/adverb as hyponym.
Of course, similar tests can be used in the reverse cases.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (9)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
25
Hyperonymy/Hyponymy between state-denoting verbs and adjectives (or
adverbs)
Test sentence
If something/someone/it is Y then something/someone/it Xs +
AdvP/AdjP/NP/PP
If something/someone/it Xs then something/someone/it is in a certain state of
being Y
- X is a verb in the third person singular form
- Y is an adjective
- there is at least one specifying AdvP, NP or PP that applies to the X-phrase
- preferably there is no morphological link between the adjective and the verb
If someone is horrified then someone fears something intensively
* If someone fears something then someone is in a certain state of being horrified
horrified (Y)
HAS_XPOS_HYPERONYM
to fear (X)
to fear (X)
HAS_XPOS_HYPONYM
horrified (Y)
2.4 Antonymy
Although the antonymy (or opposition) relation is not always indicated in the sources which we
are using, Fellbaum (1993:49 ff) provides evidence that it is a particularly psychologically salient
relation for verbs. Its semantics is complex and actually there is no general agreement on the
terminology used (e.g., Lyons 1968, 1977 distinguishes among three types of ‘meaning oppositions’
and he uses the term ‘antonymy’ to refer just to one of these three types, but other authors make
different distinctions). However, the notion we are referring to in our work seems to correspond more
to what Lyons calls in general ‘opposition’, that is to a more loosely defined notion. If synonymy has
been defined as the equivalence of meaning in a context, antonymy can be defined as the opposition of
meaning in a context.
Much of the oppositions among verbs are revealed by morphological elements: tie/untie,
apparire/sparire (appear/disappear), approvare/disapprovare (approve/disapprove), etc. However, in
other cases, the antonymy rises from the opposition existing between adjectives incorporated within the
meaning of verbs: abbellire/imbruttire (prettify/uglify), dimagrire/ingrassare (slim/fat), etc. Still other
verbs are then opposed because there is opposition between different kinds of semantic components
incorporated within their meanings: for instance, if we take into consideration the hyponyms of the
Italian verb andare (to go), we can see that within the meaning of certain verbs the hyperonym is
conflated with semantic components denoting opposed direction (e. g., entrare/uscire - to go in/to go
out, salire/scendere - to go up/to go down). Other pairs, considered to be antonymous in WN 1.5, are
not found within the same taxonomies, neither can be identified by means of morphological markers;
however, they occur within the same semantic field and “refer to the same activity, but from the
viewpoint of different participants” (Fellbaum 1993:51): lend/borrow, teach/learn, buy/sell, etc.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
26
Given our notion of antonymy, the general test used to state that two verb synsets are
antonymous is the following, ‘bidirectional’ test:
X is the opposite of Y
Which means that both the following sentences must be true, in order the two verb synsets be
antonymous:
- He Xs entails that he does not Ys
yes
- He Ys entails that he does not Xs
yes
Clear yes-yes = antonymy
A test like this, however, needs to receive some further constraints which avoid obtaining as a
result two verbs contrasting in some of their semantic features but not really definiable as ‘antonyms’.
For instance, the test above might work with X= eat and Y= sleep, since the two verbs refer to
processes which can never occur at the same time (since the former requires that the protagonist is
actively involved, whereas the latter requires a ‘passive’ protagonist), even if they are not each other’s
opposites. In fact, antonyms typically form contrasting categories within the same dimension. This
means that a verb not only contrasts with an antonym-verb in one or more features (e.g. active
protagonist/passive protagonist) but that they often have to share the same hyperonym: i.e. they have to
be competitors within a reasonable denotational range. This latter criterion prevents us from contrasting
irrelevant pairs such as eat and sleep.
There is, however, also the possibility to have antonyms not occurring within the same
taxonomy; in this case, either their hyperonyms are antonyms or the two verbs display a particular
relation: they share the main participants, but these have opposite roles in the situations10 referred to by
the verbs themselves. For instance, vendere (to sell) and comprare (to buy) can be said to be
antonymous because they are respectively hyponyms of cedere (to give) and ricevere (to receive),
which are antonymous. The latter two verbs, then, are found at the top of their taxonomies, thus their
antonymy is revealed by the fact that the indirect object of cedere, i.e. the addressee involved in the
event referred to by this verb, is the subject (protagonist) of ricevere.
The main information source for the antonymy relation are dictionary definitions. It is often the
case that the definiendum is described in terms of its verbal opposite(s). In Italian, e.g., the antonymy
relation can be formally characterized by the presence of the adverbial non (‘not’) pre-modifying the
verbal head of a definition: this adverbial is used to trigger the identification of such relation; the value
of the relation, i.e. the antonym of the definiendum, is the definition head. However, this is not the only
way in which pairs of antonyms can be acquired from dictionary verb definitions: other more complex
strategies can be envisaged for acquiring this kind of relation, which imply the simultaneous
10
The word ‘situation’ is used throughout the document to generically refer to the states or events or processes referred to
by verbs. In keeping with von Wright (1963) and others, we shall, moreover, use i) the term ‘state’ to refer to a state of
affairs; ii) the term ‘event’ to refer to a change of state; iii) the term ‘process’ to refer to sequences of micro-events and
states, felt, though, at the linguistic level, as homogeneous ongoing situations (e.g., the situation referred to by the verb to
write). The term ‘actions’ will also be used, to refer both to agentive events and agentive processes.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
27
consideration and comparison of different verb entries. For instance, within the Italian LDB the two
change-of-state verbs rendere (to make) and diventare (to become) are the hyperonyms of many verbs,
in whose definitions they occur together with adjectives indicating the result of the change (e.g.,
abbellire (to prettify) is defined as “rendere bello” (to make beautiful), and ingrassare (to get fat) is
defined as “diventare grosso” (to become fat)). Since the (eventual) opposite adjective is usually
indicated in the definitions of adjectives, a procedure could be developed which retrieves all the
oppositions between pairs of adjectives and searches, then, for the same oppositions in the definitions
of the verbs within the taxonomies of the two genus verbs under analysis. Such a procedure would
return, e.g., a verb like imbruttire (to uglify), defined as “rendere brutto” (to make ugly), as antonym of
the above mentioned abbellire, and dimagrire (to slim), defined as “diventare magro” (to become
slim), as antonym of ingrassare. Similar methodologies of automatic analysis could be used for
retrieving verbs whose antonymy is caused by the opposition of incorporated adverbs or prepositions
(e.g., entrare = andare dentro (to go in) / uscire = andare fuori (to go out)).
Since it has been noted that sometimes there can be opposition only between two members of
different synsets rather than between the whole synsets (this has been noted in particular for nouns, cf.
D005), within the database also antonymy between synset members will be allowed, besides antonymy
between synsets, called, then, ‘near-antonymy’.
2.4.1 ANTONYMY
In the following general formal criteria for the definition of antonymy between single members of
synsets are given.
Verb test (10)
Comment:
Score
yes
a
yes
b
Conditions:
Effect:
LE2-4003
Antonymy between single variants in verb synsets (a)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a synset variant in the third person singular form
- Y is a synset variant in the third person singular form
- X and Y are members of co-hyponym synsets
X
ANTONYM
Y
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (11)
Comment:
Score
yes
a
yes
b
Conditions:
Effect:
Verb test (12)
Comment:
Score
yes
a
yes
b
Conditions:
Effect:
28
Antonymy between single variants in verb synsets (b)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a synset variant in the third person singular form
- Y is a synset variant in the third person singular form
- there is a hyperonym of X which is opposite to a hyperonym of Y
X
ANTONYM
Y
Antonymy between single variants in verb synsets (c)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a synset variant in the third person singular form
- Y is a synset variant in the third person singular form
- the situation referred to by X has an addressee and the addressee is the
protagonist of the situation referred to by Y
X
ANTONYM
Y
2.4.2 NEAR_ANTONYMY
In the following general formal criteria for the definition of antonymy (‘near-antonymy’) between verb
synsets are given.
Verb test (13)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Antonymy between verb synsets (a)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- X and Y are co-hyponym synsets
If he gets fat then he does not get thin
If he gets thin then he does not get fat
{to get fat, to put on weight}
NEAR_ANTONYM
{to get thin, to lose weight}
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (14)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Verb test (15)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
29
Antonymy between verb synsets (b)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- there is a hyperonym of X which is opposite to a hyperonym of Y
If he sells then he does not buy
If he buys then he does not sell
{to sell, to exchange for money} NEAR_ANTONYM
{to buy, to purchase, to take}
Antonymy between verb synsets (c)
Test sentence
If something/someone/it Xs then something/someone/it does not Y
If something/someone/it Ys then something/someone/it does not X
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- X has an addressee and the addressee is the protagonist of Y
If he gives then he does not take
If he takes then he does not give
{to give}
NEAR_ANTONYM
{to take, to take away}
EuroWordNet
30
EuroWordNet D006: Definition of the links and subsets for verbs
2.4.3 XPOS_NEAR_ANTONYMY
As said above, also antonymy between different POS is allowed, as in the cases of synonymy
and hyperonymy/hyponymy. However, this antonymy relation is only allowed between synsets (and
not variants). Here below, some of the criteria for its identification are provided, i.e., those taking into
consideration co-hyponym antonyms; for the other cases, cf. the tests given for near-antonymy.
Verb test (16)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Verb test (17)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Antonymy between verbs and nouns denoting events or processes
Test sentence
If something/someone/it Xs then a/an Y does not take place
If a/an Y takes place then something/someone/it does not X
- X is a verb in the third person singular form
- Y is a noun in the singular
- X and Y are (XPOS) co-hyponyms
If someone falls asleep then awakening does not take place
If awakening takes place then someone does not fall asleep
{to fall asleep} (X)
XPOS_NEAR_ANTONYM
{awakening} (Y)
Antonymy between verbs and nouns denoting states
Test sentence
If something/someone/it Xs then something/someone/it is not in a state of Y
If something/someone/it is in a state of Y then something/someone/it does not X
- X is a verb in the third person singular form
- Y is a noun in the singular
- X and Y are (XPOS) co-hyponyms
If someone loves someone then someone is not in a state of hate
If someone is in a state of hate then someone is not loving
{to love} (X) XPOS_NEAR_ANTONYM {hate} (Y)
EuroWordNet
31
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (18)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Antonymy between verbs and adjectives (or adverbs)
Test sentence
If something/someone/it Xs then something/someone/it is not Y
If something/someone/it is Y then something/someone/it does not X
- X is a verb in the third person singular form
- Y is an adjective
- X and Y are (XPOS) co-hyponyms
If someone sleeps then someone is not awake
If someone is awake then someone does not sleep
{to sleep} (X) XPOS_NEAR_ANTONYM {awake} (Y)
2.5 Involved
As mentioned above (cf. § 2.1), to avoid ‘shallow hierarchy structures’, additional relations,
linking verbs to other parts of speech, were defined in order to encode information which can be found
in the differentiae of dictionary definitions, especially with respect to verbs, and which can be very
useful from the viewpoint of the EuroWordNet system user.
One of these relations has been named ‘Involved’ (and its counterpart ‘Role’) and indicates a
link between a verb and a noun (or, in some cases, an adjective or an adverb - cf. footnote 7) whose
meaning is ‘incorporated’ in, or connected with, the meaning of the verb itself. The subtypes of the
relation are reported again in the following:
INVOLVED
involved-agent
involved-patient
involved-instrument
involved-location
involved-direction
involved-source-direction
involved-target-direction
The importance of coding information of this kind can be inferred also by an analysis of the
most recent theoretical developments in lexical semantics. In fact, research in this field has
demonstrated that there is often a direct connection among the arguments lexicalised within a verb root
and its syntactic properties (cf., e.g., Levin 1993). Furthermore, it has been shown that, since (groups
of) languages display different ‘preferences’ for patterns of lexicalisation of semantic components in
verb roots (Talmy 1985), in order to develop LE systems these should be taken into due consideration
(cf. Sanfilippo et al. 1992 for a description of the use of such data for MT). The specific subrelations
chosen, then, have not ‘arbitrarily’ been chosen, but are the most prominent ones to describe the
different semantic references involved in the meaning of a word. Indeed, besides providing the
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
32
possibility of encoding data on involved agents and patients, which are clearly relevant with respect to
every class of verbs, these links both allow coding data on every kind of means used to perform an
action (in fact ‘instrument’ is used here in a wide sense), and assume Gruber (1976) view according to
which the semantics of motion and collocation can be seen as providing an interpretation for many
semantic classes of verbs (besides the motion ones), e.g. verbs of bringing, saying, giving.
Since the subtypes of the relation will be used to encode data on arguments strongly ‘involved’
in the meaning of a verb, and generally indicated within dictionary definitions, the labels used,
although already ‘familiar’ to linguists because often associated with ‘thematic roles’, should not be
understood as referring to the arguments which a verb can take, i.e. with the arguments co-occurring
with it in a sentence. Indeed, the whole database is being built to encode data on the semantic content
of words, described by means of a ‘relational’ approach, and our links will encode (only) data on the
semantic features of arguments incorporated in the meaning of a verb, which certainly determine also
the kind of arguments which a verb allows as its complements, but which do not exactly coincide with
them. For instance, whereas in the meaning of a verb like to move, allowing both agent and patient
arguments, there is no inherent reference either to a particular ‘involved-agent’ or to an
‘involved-patient’ (because, in fact, many kind of ‘objects’ can move), which therefore should not be
encoded, within the meaning of a verb like sgambettare (an Italian verb meaning to kick one’s legs
about and only used to refer to a movement performed by babies) there is an incorporated
‘agent-protagonist’ of the event that will be encoded by means of the relation INVOLVED_AGENT
--> babies.
Thus, according to our proposal, the various subtypes of the main relation should receive a
value only in case there is a clear reference to a particular link within the meaning of a verb. As a
matter of facts, as it happens for all the relations, also this link has a correspondent reverse relation
which connects nouns to verbs and is called ‘ROLE’ relation (cf. Deliverable D005). Thus, although
only the connection between a verb and an argument strongly involved in its meaning should be
encoded by means of the INVOLVED relation, whenever a noun will be linked to a verb by means of
the ROLE relation also the reverse relation will automatically be derived (but, in this case, a ‘reversed’
label will be added). For instance, the verb to hammer inherently involves only the noun hammer,
which will be linked to it by means of the INVOLVED_INSTRUMENT relation. Though, the noun
carpenter can be connected with the verb to hammer by means of the ROLE_AGENT relation, so that
the correspondent link from the verb to the noun (i.e., to hammer --> INVOLVED_AGENT -->
carpenter) could be automatically derived. In this case, as also in the case of all the ‘default’ reversals
of relations, we shall automatically insert the label ‘reversed’ and allow disjunction of agents, in order
to ensure the possibility of having multiple (possible) agents connected with a verb.
In the following, we provide general criteria for the identification both of the main relation and
of its subtypes. The test for the general relation (which should also apply to any subtype) is the
following:
Verb test (19)
Comment:
Score
LE2-4003
Involvement relation in general
Test sentence
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
yes
a
Conditions:
Example:
Effect:
a
33
(A/an) X is the one/that who/which is involved in Y
- X is a noun
- Y is a verb in the gerundive form
An hammer is that which is involved in hammering
{to hammer} (Y)
INVOLVED
{hammer} (X)
The next tests can then be used to elicit more specific involvements:
Verb test (20)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Verb test (21)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Agent Involvement
Test sentence
(A/an) X is the one/that who/which does the Y
- X is a noun
- Y is a verb in the gerundive form
A teacher is the one who does the teaching
{to teach} (Y) INVOLVED_AGENT
{teacher} (X)
Patient Involvement
Test sentence
(A/an) X is the one/that who/which undergoes the Y
- X is a noun
- Y is a verb in the gerundive form
A learner is the one who undergoes the learning
{to learn} (Y) INVOLVED_PATIENT
{learner} (X)11
11
It should be clear that the fact that we encode either an INVOLVED_PATIENT or an INVOLVED_AGENT relation for
a pair verb/noun does not indicate that the one encoded is the only possible relation between the two, but only that it is the
more directly involved one. In fact, even if a learner undergoes the learning, and is thus the INVOLVED_PATIENT of the
verb, he can do it actively, i.e. teaching himself, and be also an agent in this sense. However, the former relation is that
strongly implied by the meaning of the verb. In any case, the two roles AGENT and PATIENT could also be encoded as a
conjunction of the two relations whenever this is necessary (i.e., directly involved in the meaning of a verb). For instance, in
the case of verbs referring to motion by the protagonist of the event indicated, the protagonist is at the same time actively
and passively involved in the movement referred to by the verb: e.g., a runner is the agent of the process indicated by the
verb to run, but he is also the ‘patient’ of the same process.
LE2-4003
EuroWordNet
34
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (22)
Comment:
Score
yes
a
Conditions:
Example (1):
Effect:
Example (2):
Effect:
Verb test (23)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
LE2-4003
a
Instrument Involvement (also valid for nouns indicating things, used for doing
something, which cannot be really called ‘instruments’)
Test sentence
(A/an) X is either i) the instrument that or ii) what is used to Y (with)
- X is a noun
- Y is a verb in the infinitive form
An hammer is the instrument that is used to hammer
{to hammer} (Y)
INVOLVED_INSTRUMENT
{hammer} (X)
A sailing boat is what is used to sail with
{to sail} (Y)
INVOLVED_INSTRUMENT
{sailing boat} (X)
Location Involvement
Test sentence
(A/an) X is the place where the Y happens
- X is a noun
- Y is a verb in the gerundive form
A school is the place where the teaching happens
{to teach} (Y) INVOLVED_LOCATION
{school} (X)
EuroWordNet
35
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (24)
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Verb Test (25)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Direction Involvement12
Test sentence
It is possible to Y from/to a place
- Y is a verb in the infinitive form
It is possible to run from/to a place
{to run} (Y)
INVOLVED_DIRECTION
{place} (X)
Source-Direction Involvement
Test sentence
(A/an/the) X is the place from where Ying happens / one Ys
- X is a noun
- Y is a verb
The start is the place from where the racing happens
{to race} (Y) INVOLVED_SOURCE
{the start} (X)
12
In the same way as there is the possibility to identify a generic INVOLVED relation for all those cases of undecidable
kind of involvement, there is the possibility of indicating the existence of a generic involved direction in the meaning of a
verb. Indeed, some verbs (at least in Italian) inherently refer to change-of-position (i.e., motion from/to a non-specified
place), without, actually, being inherently explicit with respect to the exact direction of motion. E.g., correre (to run)
displays two main senses (indicated by different auxiliary selection): one of undirected motion (2a below) and one of
directed motion (2b). The specific direction of the running is however non-specified within the meaning of the verb and can
thus be variously specified by its complements:
2a. Ho corso per un'ora / * a casa ('I HAVE run for one hour / * (to) home')
2b. Sono corso a casa / via da casa ('I BE run away from home / home')
In cases like these we can state an INVOLVED_DIRECTION relation with the noun luogo (place), without indicating a
relation with a more specific noun. The INVOLVED_DIRECTION relation is thus useful in order to distinguish both
among different kinds of incorporation in a language (e.g., the Italian verb nuotare (to swim) has no
INVOLVED_DIRECTION) and among differences of lexicalisation across languages (e.g., to swim has a generic
INVOLVED_DIRECTION), thus satisfying the need for the identification of differences in lexicalisation patterns
emphasized within the TA, where it is stated that [this resource] “will enhance the fundamental understanding of
lexicalisation patterns across languages which will be crucial for machine translation and language learning systems” (p.
17).
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
Verb Test (26)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
36
Target-Direction Involvement13
Test sentence
(a/an/the) X is the place to which Ying happens / one Ys
- X is a noun
- Y is a verb
The ground is the place to which one collapses/falls heavily
{to collapse, to fall heavily} (Y)
INVOLVED_TARGET_DIRECTION
{ground} (X)
Mono- and bi-lingual dictionaries both contain relevant information connected with the
INVOLVED relation; although the data related with typical arguments conflated within the meaning of
a verb are generally found in definitions, also example sentences and semantic indicators may be used
to acquire them. Furthermore, useful information is not only provided by verb entries: indeed, it is
sometimes possible to use information found within the entries of nouns related to verbs. For instance,
in the definitions of instruments there is generally the indication of the action that they are used for.
Since it is not always possible to identify patterns uniquely connected with one subtype of the
relation, the acquisition procedure cannot be fully automatic: in fact, heuristics have to be developed
which return an ‘intermediate’ value for a relation-subtype, and which always require a manual
verification (see Montemagni 1995 on the problems of the automatic identification of the thematic role
associated with verb arguments, and Alonge 1992a, 1992b for proposals related to the development of
heuristics for extracting such data).
By adding in the EWN model such a relation we give the possibility to code much information
(generally found in dictionaries) which is useful to outline the semantics of a verb. Probably these
relations will be only partially given values in our database (for the time being), however we think that
it is necessary to have the possibility to add information of this kind (for instance, probably we are
going to code some data of this kind for those taxonomies for which we had extracted them in
Acquilex, but we do not think we shall be able to extract these relations for all the verbs which will be
taken into consideration).
2.6 ‘Causes’ and ‘Is_Caused_By’
13
As already claimed, all the INVOLVED relations may help coding data on the similarities/differences in lexicalisation
patterns across languages. As far as the latter two are concerned, for instance, we can see that the Italian verb sbarcare
displays an INVOLVED_SOURCE_DIRECTION relation with barca and has a direct corresponding verb in English: to
disembark related to the INVOLVED_SOURCE_DIRECTION boat. On the other hand, the Italian verb rincasare (to go
back home), connected with the noun casa by means of the relation INVOLVED_TARGET_DIRECTION, has no direct
correspondent in English.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
37
The causal relation is used in WN 1.5 to indicate the relation between two verbs one of which
referring to an event causing a resulting event, process or state referred to by the second verb (like in
the case of show/see, fell/fall, give/have). The relation can be identified by means of the following
general test:
He Xs causes He Ys.
Which means that the following sentences must be respectively true and false before two verbs can be
connected by means of a causal relation:
- He Vs1 causes Vs2
yes
- He Vs2 causes Vs1
no
Clear yes, no = causal relation.
Whereas in WN 1.5 the causal relation only holds between verbs and, furthermore, only should
hold (according to what stated in Fellbaum 1993: 54) between verbs referring to temporally disjoint
situations, within our database this relation will be used also to link verbs and higher-order nouns
denoting events or processes (henceforth ‘dynamic situations’ or dS) to verbs, higher-order nouns,
adjectives or adverbs. In addition, we have distinguished among three possible cases of temporal
relationship between the (dynamic/non-dynamic) situations indicated by the verbs (/other POSs)
linked:
•
•
•
a cause relation between two situations which are temporally disjoint: there is no time point when
dS1 takes place and also S2 (which is caused by dS1) and vice versa (e.g., in the case of to shoot/to
hit);
a cause relation between two situations which are temporally overlapping: there is at least one time
point when both dS1 and S2 take place, and there is at least one time point when dS1 takes place
and S2 (which is caused by dS1) does not yet take place (e.g., in the case of to teach/to learn);
a cause relation between two situations which are temporally co-extensive: whenever dS1 takes
place also S2 (which is caused by dS1) takes place and there is no time point when dS1 takes place
and S2 does not take place, and vice versa (e.g., in the case of to feed/to eat).
It is interesting to note that in the last case the causal relation may be sometimes hardly
distinguished by the hyponymy relation (cf. the above reported Dutch verbs dichttrekken/sluiten).
Thus, it becomes important to have tests which allow to distinguish among the different kinds of
temporal relationships between the verbs being related: indeed, in case two words refer to situations
which are co-extensive, the two words should also be tested for hyponymy and if this relation applies,
we decided that the cause relation should not be expressed.
As we have already recalled, then, different types of causality can also be distinguished with
respect to the factivity of the effect (following Lyons, 1977):
•
factive: situation dS1 implies the causation of S2, e.g. “to kill causes to die”;
LE2-4003
EuroWordNet
38
EuroWordNet D006: Definition of the links and subsets for verbs
•
non-factive: situation dS1 probably or likely causes S2 or S1 is intended to cause some situation S2
“to search may cause to find”.
Thus, while the differences in the temporal relationships between the situations referred to by
the words being linked will only be taken into account by means of different tests formulated to
identify the existence of the causal relation, the factivity/non-factivity of the link will be encoded by
means of labels applied to the relation itself (cf. above).
In the following general formal criteria for the definition of causation relation are provided.
Verb test (27)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Factive causation relation between verbs/nouns (general)
Test sentence
(A/an) X causes (a/an) Y
(A/an) X has (a/an) Y as a consequence
(A/an) X leads to (a/an) Y
the converse of (a)
- X is a verb in the infinitive form or X is a noun in the singular
- Y is a verb in the infinitive form or Y is a noun in the singular
to kill (/a murder) causes to die (/ death)
to kill (/a murder) has to die (/ death) as a consequence
to kill (/a murder) leads (someone) to die (/ death)
*to die / (a) death causes to kill
*to die / (a) death has to kill as a consequence
*to die / (a) death leads (someone) to kill
{to kill} (X) CAUSES
{to die} (Y) factive
{to die} (Y) IS_CAUSED_BY
{to kill} (X) reversed
{to kill}
CAUSES
{death}
factive
{death}
IS_CAUSED_BY
{to kill}
reversed
{murder}
CAUSES
{to die}
factive
{to die}
IS_CAUSED_BY
{murder}
reversed
{murder}
CAUSES
{death}
factive
{death}
IS_CAUSED_BY
{murder}
reversed
Obviously, the event of ‘dying’ is not necessarily caused by ‘killing’. This may either follow
from the fact that the verb kill refers to one of the possible disjunct causes for die, or it may be
expressed by making explicitly labelling the reverse relation “dying IS_CAUSED_BY killing” as
reversed (as is done here).
The following test is for detecting factive causation relation between verbs (but also holding for nouns
referring to non-dynamic or dynamic situations) and adjectives (/adverbs) is provided:
Verb test (28)
LE2-4003
EuroWordNet
39
EuroWordNet D006: Definition of the links and subsets for verbs
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Factive causation relation between verbs and adjectives (or adverbs)
Test sentence
X causes to be Y
X has being Y as a consequence
X leads to be(ing) Y
the converse of (a)
- X is a verb in the infinitive form
- Y is and adjective
to kill causes to be dead
to kill has being dead as a consequence
to kill leads someone to be dead
*to be dead causes to kill
*to be dead has to kill as a consequence
*to be dead leads (someone) to kill
{to kill} (X) CAUSES
{dead} (Y) factive
{dead} (Y) IS_CAUSED_BY
{to kill} (X) reversed
Non-factivity is more evident in the following tests:
LE2-4003
EuroWordNet
40
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (29)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Non-factive causation relation between verbs (/nouns) using a modal auxiliary14
Test sentence
(A/an) X may cause (a/an) Y
(A/an) X may have (a/an) Y as a consequence
(A/an) X may lead to (a/an) Y
the converse of (a)
- X is a verb in the infinitive form or X is a noun in the singular
- Y is a verb in the infinitive form or Y is a noun in the singular
to search may cause to find
to search may have to find as a consequence
to search may lead (someone) to find
?to find may cause to search
?to find may have to search as a consequence
?to find may lead (someone) to search
{to search} (X)
CAUSES
{to find} (Y)
(non-factive) Dis
{to find} (X
IS_CAUSED_BY
{to search} (Y) (non-factive) Dis
This test is exactly the same as the previous one except for the inserted modal verb may which
expresses non-factivity of the cause. Non-factivity here can be indicated by adding the label ‘Dis’,
indicating that either the process indicated by to search may lead to the event indicated by to find or to
some other contrary result. In the case a pair of verbs does not pass the first test on factive causality it
may still pass a non-factive test. Some other modal constructions are used in the tests provided in the
Appendix for each language.
The tests provided so far are general tests to identify causal relation. However, we have claimed
above that some more specific tests are needed, which emphasize the eventually different temporal
reference of the words related by the causal link, in order to distinguish among cases of ‘genuine’
causal relation and cases in which we should not express the causal relation, but rather an hyponymy
relation. The following test elicits ‘genuine’ cause relation:
14
A similar test can be used for the relation to adjectives (or adverbs), and this also holds for the tests provided below.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
Verb test (30)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
41
Causation relation between verbs (/nouns) referring to temporally disjoint
situations
Test sentence
If (a/an) X takes place it causes/may cause (a/an) Y to take place afterwards/later
on
the converse of (a)
- X is a verb in the gerundive form or X is a noun in the singular
- Y is a verb in the gerundive form or Y is a noun in the singular
If sending takes place it causes receiving to take place later on
* If receiving takes place it causes sending to take place later on
{to send} (X)
CAUSES
{to receive} (Y)
factive
{to receive} (Y)
IS_CAUSED_BY
{to send} (X)
reversed
In case two words do not pass this test, but pass the following temporally-non-disjoint test, it is
necessary to test them for the hyponymy relation: if the latter applies, then we shall not express the
cause relation.
Verb test (31)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Causation relation between verbs (/nouns) referring to temporally non-disjoint
situations
Test sentence
If (a/an) X takes place it causes/may cause (a/an) Y to take place at the same time
the converse of (a)
- X is a verb in the gerundive form or X is a noun in the singular
- Y is a verb in the gerundive form or Y is a noun in the singular
- X and Y are not connected by means of the hyponymy relation
If pulling takes place it may cause opening to take place at the same time
? If opening takes place it may cause pulling to take place at the same time
{to pull} (X)
CAUSES
{to close} (Y) (non-factive)
{to close} (Y)
IS_CAUSED_BY
{to pull} (X) (non-factive)
A further issue is worth noting here. We have stated that dynamic situations may cause both
other dynamic situations or non-dynamic ones. Whether the caused result is a dynamic event/process or
a (non-dynamic) state can be inferred from the hyponymy relation of the result with
dynamic/non-dynamic hyperonyms (e.g., state or change). This can be clarified by the following
example:
LE2-4003
EuroWordNet
42
EuroWordNet D006: Definition of the links and subsets for verbs
i)
ii)
fall asleep V
fall asleep V
fall asleep V
sleep V
sleep N
asleep A
CAUSES
CAUSES
HAS_HYPERONYM
HAS_HYPERONYM
XPOS_NEAR_SYNONYM
XPOS_NEAR_SYNONYM
addormentare (make sleep) V
addormentarsi (fall asleep) V
addormentare (make sleep) V
sleep V
sleep N,
change V
be V
sleep V
sleep V
asleep A
CAUSES
addormentarsi (fall asleep) V
HAS_HYPERONYM cambiare (change) V
HAS_HYPERONYM fare (make, cause) V
In i) we see that the situation referred to by the verb to sleep is non-dynamic, as is expressed by its
hyponymy relation with the verb to be. We also see that the noun sleep and the adjective asleep have
near-synonymy relations with the verb sleep and are, therefore, non-dynamic. In ii) we see an example
in which the Italian verb addormentarsi (to fall asleep) is caused by addormentare (to make sleep).
The fact that we are dealing with two dynamic situations is again expressed by the hyponymy relation.
From the hyponymy relation may be also inferred that addormentarsi is a ‘non-controlled’ process and
addormentare is a ‘controlled’ action, since their hyperonyms display those characteristics. Using the
combination of CAUSE and HYPONYMY relations, we can thus encode many different kinds of
information, while we have a maximum of flexibility to relate words across the different
parts-of-speech. The latter is very important because we will come across situations in which words
from different parts-of-speech are CAUSES or which are results-of-CAUSES and also have synonymy
relations or cross-linguistic equivalence relations (are linked to the same ILI-record) and therefore
should also have the same CAUSE-relations.
Dictionary definitions represent the main information source in the case of the causal relation.
Different patterns are used in dictionaries to convey this kind of relation, all hinging on the use of
specific constructions containing verbs indicating ‘cause’. In Italian dictionaries, like in Spanish, for
instance, the most commonly used pattern is represented by the periphrastic causative construction with
far(e) - in Spanish, hacer - (lit. ‘make’, ‘cause’) followed by a verb in its infinitival form: e.g., the
verb accagliare (to curdle) in its transitive reading is defined as far coagulare (to cause to coagulate).
This construction indicates that the verb being defined is the causative counterpart of the infinitive verb
which follows far(e); in the example at hand, accagliare is related via causation to the action referred
to by coagulare ‘coagulate’. Other more complex patterns involve the simultaneous consideration of
different definitions of the same verbal headword; this is the case of verbs undergoing both causative
and inchoative uses such as break.
LE2-4003
EuroWordNet
43
EuroWordNet D006: Definition of the links and subsets for verbs
2.7 Has_Subevent and Is_Subevent_Of
According to Fellbaum (193: 45), “the different relations that organize the verbs can be cast in
terms of one overarching principle, lexical entailment”. Two basic kinds of lexical entailment can be
distinguished: one involves ‘temporal inclusion’ (the two situations referred to by the verbs in the
relation partially or totally overlap); the other involves ‘temporal exclusion’ (the two situations are
variously temporally disjoint). These temporal relationships between verbs are taken as the basis for a
further distinction of four kinds of entailment:
a. + Temporal Inclusion
a.1 co-extensiveness (e. g., to limp/to walk)
a.2 proper inclusion (e.e., to snore/to sleep)
b. - Temporal Exclusion
b.1 backward presupposition (e.g., to succeed/to try)
b.2 cause (e.g., to give/to have)
Thus, within WN 1.5 class (a1) verbs are linked by means of the hyponymy (called - for verbs ‘troponymy’) relation; (b2) verbs are linked by means of a causal relation; and both (a2) and (a3) verbs
are linked by means of a generic ‘Entailment’ relation. That is, the latter relation is applied to those
cases that do not fall within the more specific classes of verbs linked by means of hyponymy or cause
relations.
Within our project we decided to encode data related to the WN 1.5 entailment relation in a
different way. In fact, it seems to us that the ‘backward presupposition’- entailment relation can be
expressed by using the causal relation in conjunction with the factivity label. For instance, as far as to
succeed and to try are concerned, we may state the following relation:
{to succeed} IS_CAUSED_BY
{to try}
CAUSES
{to try}
Factive
{to succeed} Non-factive
On the other hand, as far as verbs related by means of ‘proper inclusion’- entailment are concerned, we
can use a link indicating more clearly the existence of this temporal relationship between the words
involved, instead of the more general relation of ‘entailment’, which, as we have seen, is ambiguous
since every relation is a kind of entailment relation. In fact, in these cases we may speak of a kind of
verb-meronymy relation (cf. D005 for noun-meronymy) and describe it by means of the link
HAS_SUBEVENT/IS_SUBEVENT_OF. Thus, we shall have:
{to snore}
{to sleep}
{to buy}
{to pay}
LE2-4003
IS_SUBEVENT_OF
HAS_SUBEVENT
HAS_SUBEVENT
IS_SUBEVENT_OF
{to sleep}
{to snore}
{to pay}
{to buy}
reversed
reversed
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
44
In the following, general criteria for the definition of the HAS_SUBEVENT relation between
verbs (/nouns referring to events or processes) are provided:
Verb test (32)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Verb test (33)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Has_Subevent/Is_Subevent_of relation between verbs (/nouns) (a)
Test sentence
Y takes place during or as a part of X, and
whenever Y takes place, X takes place
the converse of a)
- X is a verb in the gerundive form
- Y is a verb in the gerundive form
Snoring takes place during or as part of sleeping, and
whenever snoring takes place, sleeping takes place
*Sleeping takes place during or as part of snoring
*Whenever sleeping takes place, snoring takes place
{to snore} (X) IS_SUBEVENT_OF {to sleep} (Y)
{to sleep} (Y) HAS_SUBEVENT {to snore} (X) reversed
Has_Subevent/Is_Subevent_Of relation between verbs (/nouns) (b)
Test sentence
X consists of Y and other events or processes
the converse of a)
- Y is a verb in the gerundive form
- X is a verb in the gerundive form
buying consists of paying and other events or processes
*paying consists of buying and other processes
{to buy} (Y)
HAS_SUBEVENT {to pay} (X)
{to pay} (X)
IS_SUBEVENT_OF {to buy} (Y) reversed
Just as with meronymy for nouns (see D005) there are subevents that can only occur as being
embedded within one type of a larger situation and there are subevents that may occur in different
embedding situations. Furthermore, there may be embedding situations for which the subevent is a
necessary step or phase and there may be embedding situations for which the subevent is optional.
Both these possibilities have an effect on the implication relation between the events. Since “sleep”
does not necessarily imply “snore” but “snore” always implies the embedding event “sleep” the
implication direction is from “snore” to “sleep” (and not the other way around from the embedding
event to the subevent)). However, since “buy” necessarily implies “pay” but “pay” can also be a
subevent of another embedding event, the implication relation is here from the embedding event to the
subevent (from “buy” to “pay”). This difference has however nothing to do with the subevent relation
as such but more the dependentness of the events. In the SUBEVENT relation it may be expressed by
the label reversed or by disjunction of the relation with another subevent or another embedding event.
LE2-4003
EuroWordNet
EuroWordNet D006: Definition of the links and subsets for verbs
45
Nevertheless, one has to be aware that these implicational effects may interfer with the clarity of the
intuitions aroused in the above tests.
Our main information sources, i.e. the machine readable dictionaries, do not always provide
relevant information with respect to this relation: e.g., Italian sources do it only sometimes and often in
an implicit form, while Dutch ones do provide useful data.
2.8 Be_In_State and State_Of
This relation has been created mainly to encode links between nouns that refer to anything
being in a particular state expressed by an adjective(/adverb). These nouns often have an open
denotation: i.e. they can refer to any entity to which the state applies, e.g. “the poor” refers to all
entities which are in a “poor” state. Note that these nouns are not equivalent to the states: the entities
which have the property “poor” are not states but normal first-order entities. Although we have
foreseen the possibility of encoding this relation also between verbs and adjectives/adverbs, it seems a
more important relation with respect to (first-order) nouns and therefore is described more carefully in
D005.
LE2-4003
EuroWordNet
46
EuroWordNet D006: Definition of the links and subsets for verbs
3. The definition of the verb subsets
According to the TA, a second basic issue to be dealt with within Task 4.1, assigned to WP4,
was the definition of the subsets of verbs for which relations will be encoded in the database. Thus,
first of all we have aimed at identifying a set of common ‘Basic Concepts’ for each language wordnet,
by taking into consideration the defining vocabulary of each dictionary. Secondly, criteria have been
clearly defined to extend this first subset in order to obtain the about 15,000 verb synsets to be encoded
for each wordnet, as indicated within the TA.
The final vocabulary covered in EuroWordNet should have the following characteristics:15
1) include all the concepts needed to relate other concepts (i.e., no semantic domain should be
isolated);
2) display high degree of overlapping across the languages;
3) include most frequent words in every language use (identified by means of researches on textual
corpora);
4) contain language-specific lexicalization patterns;
5) contain some specific terminology, to be used to test the ability of the EWN database to be expanded
in depth;
6) include all the Parole words, in order to facilitate the parallel usage of both databases.
The general methodology identified to achieve this coverage has been to start with the
identification of ‘base concepts’ (for a discussion of general issues connected with this identification
cf. also D005). This should enable us to focus manual effort on base concepts, and use automatic
techniques for the extensions. In order to reach maximal consensus, we are using the test and
communication for the manual work on the base concepts, and we are exchanging and coordinating the
definition patterns and extraction heuristics. After the base concepts will have been completely
identified, the selections will be extended by each group making sure that the criteria 1) through 6) are
met. The exact procedure for extending the base concept selection may vary from site to site.
3.1 On selecting ‘Base Concepts’
In order to identify a set of ‘Basic Concepts’, we have taken into consideration not simply
words at the top of hierarchies extracted from individual sources, but words more generally felt to refer
to ‘central’ concepts. Their ‘centrality’ is however reflected both in a high position in the taxonomies
extracted from each language sources, and in a high number of relations displayed. Because of their
nature, though, base concepts are generally highly polysemous, with low-quality definitions in
15
Since the general issues dealt with in this section are exhaustively discussed in D005, here we are going to provide only
some necessary information on motivations and techniques for the identification of base concepts.
LE2-4003
EuroWordNet
47
EuroWordNet D006: Definition of the links and subsets for verbs
dictionaries; on the other hand, they should be (ideally) ultimately linked to any synset in the database.
This will determine to focus manual effort on them in order to establish accurate relations, while in a
second stage of our work, we should be able to encode relations for the other subset chosen by using
(semi-)automatic techniques.
Aiming at reaching a common nucleus of Basic Concepts, we have decided to choose, for each
language, those top-verbs displaying a number of frequencies as genus verbs allowing to cover at least
the 15% of the entire source used by each group. To the first set of top-verbs we have then added all
their hyperonyms which were not already part of the selection. Our aim is obtaining high-quality first
subsets with a maximal overlapping between them, both performing manual verifications and
interchanging subsets between sites. Once this goal is achieved, it is possible to add other (more
specific) areas of the vocabulary, where automatic extraction methods are more reliable.
3.1.1 Selection of the Dutch subset
The Van Dale database Vlis is different from the traditional MRDs in that particular semantic
relations between senses are already explicitly coded. We therefore can make use of some other criteria
than the other sites. The following relations are in the Vlis database:
Semantic Relations in the Vlis database
ABBREVIATION
ANTONYM
ASSOCIATIVE
CAUSATIVE
HYPERONYM
INCHOATIVE
PARTITIVE
REFERENCE
SYNONYM
WOMAN
PREFERENCE
FORM_VARIANT
SYMBOL
Verb Senses
0
148
557
5
8231
11
0
93
5049
0
108
0
0
The database does not form a closed wordnet in which all relations are unified in a single tree or
a small set of tops. In fact, the Vlis database contains 298 ends for the verbs. The reason for making a
sense a top or end in the hierarchy are often not well-motivated (the database is still in development).
In many cases a sense is an end because the meaning was too complex (e.g. many higher-order-nouns
referring to states, conditions, events) or information was of an encyclopaedic nature (names of places,
LE2-4003
EuroWordNet
48
EuroWordNet D006: Definition of the links and subsets for verbs
people, etc..). We therefore did not just take the Vlis tops as a starting point for selecting the base
concept but we considered the following criteria to be important in order of appearance:
•
•
•
•
number of relations
position in hierarchy
Vlis top senses
frequency
In total 284 verb senses have been selected. In the next sections we explain how this selection was
achieved.
• First criterion: number of relations
Inspection of the Vlis database leads to the conclusion that the major criterion for being a base concept
seems to be the number of relations a sense has.16 To get a sensible and manageable first selection in a
consistent way, we first selected the senses with the most-frequent-relations making up around 15% of
the total amount of relations. For this purpose we constructed a table with the cumulative-percentage of
all relations, ranking the senses per number of relations. This is shown in the next table (Table 1).
16
At this stage all relations have been taken into account to constitute the importance of a concept.
LE2-4003
EuroWordNet
49
EuroWordNet D006: Definition of the links and subsets for verbs
Table 1
Number
Relations
0
1
2
3
4
5
6
7
8
10
11
12
13
14
15
16
17
18
19
......
514
28605
1to9
>9
1to10
>12
1to15
>15
of Number of Senses Number of Links
0
9709
1998
812
477
281
209
155
107
82
59
31
28
20
35
25
15
13
11
......
0
14143
13748
395
13920
237
14003
140
0
9709
3996
2436
1908
1405
1254
1085
856
820
649
372
364
280
525
400
255
234
209
......
0
28889
Percentage of total Cumulative
number of links
percentage of total
number of links
0,00%
33,61%
13,83%
8,43%
6,60%
4,86%
4,34%
3,76%
2,96%
2,84%
2,25%
1,29%
1,26%
0,97%
1,82%
1,38%
0,88%
0,81%
0,72%
......
0,00%
100,00%
0,00%
33,61%
47,44%
55,87%
62,48%
67,34%
71,68%
75,44%
78,40%
81,24%
83,49%
84,77%
86,03%
87,00%
88,82%
90,20%
91,09%
91,90%
92,62%
......
100,00%
0,00%
2,79%
1,68%
0,99%
All verb senses with more than 12 relations resulted in 237 senses (1,68% of all senses) whose relations
cover circa 15 % of all verb senses.
We realised that senses of this first selection may have hyperonyms higher up in the hierarchy
that do not have that many relations, but still can be base concepts. Therefore we generated all the
hyperonym chains up from the senses of this first selection and extended this first selection with these
senses. So our second selection contained all verbs with more than 12 relations united with all the
hyperonyms of these senses up in the taxonomy. The results are in table 2:
LE2-4003
EuroWordNet
50
EuroWordNet D006: Definition of the links and subsets for verbs
Verb senses
> 12 relations
237
hyperonyms
120
union
285
table 2
• Second criterion: position in hierarchy
This last selection still contained many senses that we would not consider as base concepts.
Another important criterion seemed to be at which level in the hierarchy a sense is positioned. The
higher a sense is located in the hierarchy the more likely it is that it is a base concept . In order to
determine the level of a sense we generated a top down taxonomy starting from the top senses for
verbs. As downward relations, relations that imply a different degree of specificity, we used the
hyponym and verb relations (the verb-relation is used to link compound verbs to their base form,
which can be seen as a form of hyponymy) for the verb taxonomy. In general, relations that do not
imply a different degree of specificity like synonymy, antonymy and causative could be disregarded for
the levels of the hierarchy. However in the Vlis database we had to consider these as well because most
of these relations, except synonymy, were allowed to have downward relations themselves. Therefore
we extended each level with these sideward relations, i.e. for verbs: abbreviation, antonymy,
associative, causative, inchoative, preference, form-variant and symbol. We disregarded the reference
relation, because it functions as a morphological link between parts of speech. The number of senses at
each level downwards and side-wards are in table 3, whereas table 4 contains the number of senses per
level, where the levels are united:
Total number of Vlis verb senses = 14.143
Number of Vlis verb tops = 298
Level
0
1
2
3
table 3
LE2-4003
number of
downwards
298
2360
3740
3653
senses number of
sidewards
268
367
480
463
senses number of senses
566
2661
4111
4029
EuroWordNet
51
EuroWordNet D006: Definition of the links and subsets for verbs
Union of verb levels
levels
0 and 1
0, 1 and 2
0,1, 2 and 3
table 4
number of senses
3146
6539
8977
percentage of all senses
22,2%
46,2%
63,5%
These levels were needed to filter out the non base concepts of our second selection. Again we used the
percentage of the total amount of senses to draw a line. The number of verb senses down to level 1 is
22 % of the total number of verb senses. We decided to take the intersection of these levels with our
first selection of most-frequently related senses.
• Intersections
Verbs with more than 12 relations and their hyperonyms = 285
intersected with the verb top down taxonomy
levels
level 0
down to level 1
down to level 2
down to level 3
table 5
intersection
(number of senses)
94
209
268
276
percentage of
selection (1165)
32,9%
73,3%
94,0%
96,8%
the
first
The cut-off point was determined by inspecting the lists of included and excluded senses per level for
too specific senses.
• Relevant Vlis top senses
So far we generated base concepts first in a bottom up manner by taking the most-frequently
related concepts, extended with their hyperonyms. This selection includes only a fragment of all the
ends/tops in the Vlis database. This is shown in the previous table 5, where the intersection of the
most-frequently-related senses ad the levels is given. Level 0 here represents the level of the Vlis ends
or tops. The following table shows the overlap of the Vlis ends with the selection so far:
Not selected Vlis ends
total of Vlis ends
verbs 298
table 6
LE2-4003
Intersection with selection so far
94
Not selected Vlis ends
204
EuroWordNet
52
EuroWordNet D006: Definition of the links and subsets for verbs
Because the Vlis taxonomy is still in development many of these ends are not real top senses,
and therefore irrelevant for our subset. To filter out the relevant ones we used the top down hierarchy
and counted all the children down from a so-called top sense (at any level). We draw the line at 10 for
both nouns and verbs. This resulted in 50 verbal Vlis ends with 10 or more senses related to it (any
depth). From these 50 senses, 14 are new verbs which have not been included in the previous selection.
The union of these results in the subtotal selection of 223 verb senses.
• Finally: addition of frequency
At AMS we only have frequency information at the word level. This appeared to be less
reliable as a criterion for a basic semantic concept. Therefore, we only included frequent words when
they also satisfy one of the other criteria: many-relations or being a Vlis end. This resulted in the
addition of another 61 verb senses. The final subset of base concepts then consists of the union of this
list with the previous result: 284 verb-senses.
• Translation of the Dutch base concepts
The above base concepts have been translated to equivalents in WordNet1.5. For each senses one or
more translation have been given. The following problematic situations have been encountered when
translating the base concepts:
1) a VlisSense is broader and corresponds with several WN1.5 senses. In that case multiple senses are
selected. This can either mean that the VlisSense is a hyperonym or that the WN sense-distinctions
are too fine-grained. Criteria for deciding that the senses are synonymous are:
• if the selected senses have the same hyperonyms or other relations
• if the selected senses have the same conceptualization (especially in those cases where
the denotation is less important or can vary).
Examples:
keuze:2 =
choice:1 0091731-n {the making of a choice}
choice:2 3929489-n {what is choosen}
2) multiple VlisSenses can be linked to a single WN sense (the opposite situation). In this case each
VlisSense is linked to the same WN sense. This either suggests that the senses are synonymous
(should belong to the same synset) or the WN sense is a hyperonym. Later on, when the wordnets
are compared it will turn out that non-synonymous senses are linked to the same ILI-record and a
similar choice has to be made as in the previous case.
LE2-4003
EuroWordNet
53
EuroWordNet D006: Definition of the links and subsets for verbs
Examples:
hout: 1 {the substance trees consists of}
= 09057553-n wood 4 {the substance and any kind of these substances}
houtsoort 1 { the kind of substance }
= 09057553-n wood 4 {the substance and any kind of these substances}
versiering: 1 {what is used to decorate}
= 02029323-n decoration
2
versiersel: 1 {what is used to decorate}
= 02029323-n decoration
2 {what is used to decorate}
In the case of “wood” we see that the meaning type of wood is lexicalized in Dutch by a separate word.
Since any word can refer to some entity or the type of this entity this is not expressed in the English
meaning of “wood”. In the case of “decoration” we see that both words in Dutch have exactly the same
meaning and thus have to be joined in the same synset as full equivalents.
3) a VlisSense represents an intermediate level between a WN sense and its hyperonym. The WN
sense is too specific and the hyperonym is too general.
Example:
toon: 1 (too narrow) 04471153-n tone
(to broad)
04731716-n
stem: 2
(too narrow) 03477899-n
(too narrow) 04599795-n
(to broad)
04731716-n
7
sound
voice
voice
sound
5
1
5
5
In this case both the more specific and the more general WN synset have been linked to the Dutch
sense.
4) a VlisSense is equivalent or has a strong relation to a synset with another POS:
arme: 1 Noun BE_IN_STATE
01550906-a poor
5 Adjective
(poor people)
verval: 1Noun XPOS_NEAR_SYNONYM 00122638-v decline 5 Verb
vastzitten: 1 Verb
LE2-4003
XPOS_NEAR_SYNONYM 00131922-a
stuck
1 Adjective
EuroWordNet
54
EuroWordNet D006: Definition of the links and subsets for verbs
5) A good clue for equivalence are the sets of hyponyms, meronyms, etc., linked to a
VlisSense-WNSense pair. However, there can be two problems here:
• the downward relations match but the definition and/or upward relations (the meanings) do
not.
• the meanings and upward match very well but the hyponyms, meronyms etc.. differ
considerably.
Here the definitions and upward relations (the true meaning of the sense and not its classifying role)
should be taken as a starting point. The difference in classification can be due to other factors.
Hyponyms can be classified in different ways (e.g. by function or constitution) or at different levels.
The latter is very likely because WN uses many technical intermediate levels. These differences in
classifying will follow from the comparison of the wordnets.
In some cases the suggested translations seems inappropriate. In that case it helps to browse up and
down the taxonomy to find alternatives. More specifically it helps to seen whether the translation of a
hyponym is linked to a hyperonym in WN which is more appropriate. Translation-equivalence can thus
also be generated in a bottom-up fashion.
Another general observation is that the meanings in WordNet1.5 are often differentiated whereas only
a single or less meanings are given in the Dutch source. For example, the Vlis only gives one sense for
“schoonmaken” (clean) which corresponds with about 4 senses of “clean” in WordNet1.5 which have
more or less the same hyperonyms and are only differentiated for the object of cleaning:
schoonmaken 1
00023287-v
00109110-v
676 52
clean 1
clean 4
Level01
00106393-v
00881979-v
clean 3
clean 6
9 senses of the verb clean
Sense 1
clean, make clean -- (make clean by removing dirt, filth, or unwanted substances from; "Clean the stove!"; "The dentist
cleaned my teeth")
=> change, alter -- (cause to change; make different; cause a transformation; "The advent of the automobile may
have altered the growth pattern of the city"; "The discussion has changed my thinking about the issue")
Sense 2
clean, pick -- (remove unwanted substances from, such as feathers or pits, as of chickens or fruit; "Clean the turkey")
=> remove, take, take away -- (remove something concrete, as by lifting, pushing, taking off, etc.; or remove
something abstract; "remove a threat"; "remove a wrapper"; "Remove the dirty dishes from the table"; "take the gun from
your pocket")
Sense 3
houseclean, house-clean, clean house, clean -- ("She housecleans every week")
=> tidy, tidy up, clean up, neaten, straighten, straighten out, square away -- ("Tidy up your room!")
=> order, bring order to, bring order into
LE2-4003
EuroWordNet
55
EuroWordNet D006: Definition of the links and subsets for verbs
=> arrange, set up
=> put, set, place, pose, position, lay -- (put into a certain place: "Put your things here"; also with
abstract objects and locations: "Place emphasis on a certain point")
=> move, displace, make move -- (cause to move)
Sense 4
cleanse, clean -- (clean one's body or parts thereof, as by washing; "clean up before you see your grandparents"; "clean your
fingernails before dinner")
=> groom, neaten -- (care for the external appearance)
=> beautify, embellish, prettify -- (make more beautiful)
=> better, improve, amend, ameliorate, meliorate, make better
=> change, alter -- (cause to change; make different; cause a transformation; "The advent of the
automobile may have altered the growth pattern of the city"; "The discussion has changed my thinking about the issue")
Sense 5
clean -- (be cleanable; "This stove cleans easily")
=> be, have the quality of being -- (copula, used with an adjective or a predicate noun: "John is rich"; "This is not a
good answer")
Sense 6
clean, strip -- (remove all contents or possession from, or empty completely; "The boys cleaned the sandwich platters";
"The trees were cleaned of apples by the storm"; deprive wholly of money in a gambling game, robbery, etc.; "The other
players cleaned him completely")
=> remove, take, take away -- (remove something concrete, as by lifting, pushing, taking off, etc.; or remove
something abstract; "remove a threat"; "remove a wrapper"; "Remove the dirty dishes from the table"; "take the gun from
your pocket")
Sense 7
clean -- (remove in making clean; "Clean the spots off the rug")
=> remove, take, take away -- (remove something concrete, as by lifting, pushing, taking off, etc.; or remove
something abstract; "remove a threat"; "remove a wrapper"; "Remove the dirty dishes from the table"; "take the gun from
your pocket")
Sense 8
scavenge, clean, remove unwanted substances from -- (as in chemistry)
=> remove, take, take away -- (remove something concrete, as by lifting, pushing, taking off, etc.; or remove
something abstract; "remove a threat"; "remove a wrapper"; "Remove the dirty dishes from the table"; "take the gun from
your pocket")
Sense 9
clean -- (remove shells or husks from; "clean grain before milling it")
=> remove, take, take away -- (remove something concrete, as by lifting, pushing, taking off, etc.; or remove
something abstract; "remove a threat"; "remove a wrapper"; "Remove the dirty dishes from the table"; "take the gun from
your pocket")
Likewise, we see that all the hyponyms of the Dutch “schoonmaken” are scattered over the different
senses of “clean” in WordNet. This shows that the possibility of combining the resources using the
equivalence relations gives a lot of input for improving relations and sense-distinctions.
LE2-4003
EuroWordNet
56
EuroWordNet D006: Definition of the links and subsets for verbs
In those cases that there was a complex-equivalence relation we have now simply listed the relevant
WN synsets. The exact relation will come out in the next phase where the extracted relations of the
Dutch wordnet will be compared with WN1.5. As suggested above some of these relations can be
extracted automatically when comparing the wordnets.
The file with the Dutch verbal base concepts consist of the following information separated by tabs:
•
•
•
•
•
•
•
•
•
•
•
•
the dutch word
be\i”nvloeden
the dutch sense
1
frequency in corpus
2629
the number of relations
24
the position in the Vlis Hierarchy VlisNoEnd
the translation in the bilingual
influence,_affect
the wordnet file position followed by the POS 00435835-v
the wordnet word selected
influence
the sense number in the AMS database 7
the wordnet file position followed by the POS 01436060-v
the wordnet word selected
influence
the sense number in the AMS database 8
3.1.2 Selection of the Italian subset
As far as Italian is concerned, we have begun our search by taking into consideration data
encoded on synonymy and hyperonymy/hyponymy within our LDB. 17 In order to identify candidate
verbs, we have looked for items i) displaying a high frequency as hyperonyms, ii) displaying a high
position in taxonomic hierarchies. The whole process, however, had to be performed together with a
manual sense disambiguation of the hyperonyms themselves. Indeed, within our LDB we can only find
indications of synonymical or taxonomic relations between a verb entry and a verb homograph, i.e.
without any indications of the (verb) sense which is hyperonym (or synonym) of a verb entry;
therefore, for every occurrence of a verb as genus, we have to verify the sense in which it is used in the
definition under analysis. For instance, the most frequent genus found in our LDB is the verb fare (to
do, to make), occurring 374 times as a hyperonym: by analysing randomly selected definitions
containing it, we identified at least three fundamental different senses of the verb (mapping, in fact, to
three different verb synsets in WN 1.5); in any case, our results, at the present stage, need to be refined
by means of a more careful considerations of all the definitions containing fare. In a similar way, after
identifying a first set of verbs with a number of frequencies >20, since the whole subset of verb
definitions that we had to take into consideration was too great, we only performed a sense
disambiguation of a part of the hyperonym verb occurrences, so that the whole data we have obtained
17
Actually, in Pisa a LDB is being used which consists of a set of mono- and bi-lingual machine readable dictionaries
integrated with results of various analyses (e.g. taxonomical information) and procedures for merging the information
between dictionaries.
LE2-4003
EuroWordNet
57
EuroWordNet D006: Definition of the links and subsets for verbs
so far should be considered in a preliminary form, and, more important, the available data on frequency
refer to the senses of a verb altogether.
In any case, this first manual disambiguation performed on the most frequent hyperonym senses
has led us to choose some candidate verb senses among them, and i) to connect them with synonyms,
thus building synsets; ii) to identify their positions within taxonomic hierarchies which were manually
built while performing the sense disambiguation. Actually, the manual work to do in order to carry out
these tasks is not trivial, because of the general ‘imprecision’ of our source (and of dictionaries in
general). In fact, when disambiguating or building synsets/taxonomies, one has i) to verify the
definitions given within the same source for the hyperonym homographs under analysis: this may lead
to add, or correct, ‘senses’ in some way; ii) to eliminate problems of circularity, incoherence,
inconsistency, etc. Thus, for the time being, among all the hyperonyms whose frequency as genus
verbs is >20, we have preliminarily identified 75 undisambiguated verbs, which, after a first manual
disambiguation of their senses, seem to correspond to 101 verb senses (as alread remarked, a more
careful sense disambiguation is still needed). This resulted in a total set of 4,480 hyponymy relations
(number of frequencies of the verbs as hyperonyms) within the LDB, which correspond to about the
30% of all the hyponymy relations which are encoded in our monolingual LDB. Perhaps not all the
verb senses identified should be considered as ‘basic concepts’, even if most of them seem to play a
central role in the language. However, until a more careful sense disambiguation is not obtained, a
definitive list of ‘base concepts’ cannot be developed either. In any case, the final list will certainly
contain a higher percentage with respect to the 15% requested as the minimum percentage of relations
to be covered with the base concepts. (In the Appendix the complete list of base concepts identified so
far is provided.)
In a further stage of our work, we tried to map our base concept candidates to WN 1.5,
encountering however some different kind of problems:
•
•
•
sometimes within WN two/more senses are distinguished which seem to map to one Italian verb
sense and viceversa: e.g., the synset constituted by {avere, provare, sentire} seems to match both
to sense 1 and to sense 3 of to feel; on the other hand, both senses of diminuire match to sense 1
of to lessen. In these cases, either the one sense in a language, corresponding to multiple senses
in the other, can actually be considered a hyperonym sense of them (and a gap remains, due to
differences in lexicalisation patterns), or the distinctions in one source are simply too
fine-grained;
sometimes no corresponding sense was found (e.g., sense 1 of dare);
the levels of matching verbs within the two different languages hierarchies are sometimes very
different (e.g., while esprimere is top-1 in our source, i.e. it has one level above, the (apparently)
corresponding sense 1 of to express in WN is top-4).
As it is clear, at the present stage our work can only be considered as ‘work in progress’ and
needs to be refined when both the sense disambiguation work and the development of taxonomic
hierarchies will be completed. As far as the problem of the mapping with WN is concerned, specific
LE2-4003
EuroWordNet
58
EuroWordNet D006: Definition of the links and subsets for verbs
choices have to be taken; therefore, the correspondences which we indicate in the section on Italian
Base Concepts within the Appendix should be considered also preliminary.
3.1.3 Selection of the Spanish subset
For the definition of the first subset of major concepts we have followed a metodology based on
both frequency and comparison to WordNet 1.5. For ‘major concepts’ we should understand those
which are both more frequently occurring in language and more relevant from a taxonomic point of
view. The first requirement is covered by looking at frequencies. The second one by looking at
Spanish equivalence of the uppermost nodes in a pre-existing conceptual taxonomy -namely WN1.5.
With respect to frequencies we have decided to consider two complementary sources:
unrestricted corpus and dictionary used as a corpus. In the first case because obviously high
frequencies in corpora accounts for a large use of a word in the language. In the second case, high
frequency of occurences in dictionary accounts for a high rate of relevance of a word in the
organisation of lexical knowledge.
Therefore, the procedure detailed below has been followed:
1.- The most frequent verbs occurring in the corpus of definitions of our available MRD have been
extracted.
2.- The most frequent verbs occuring in LEXESP (a tagged 3.000.000 word corpus covering balanced
areas and registers of the language) have also been extracted.
3.- Subsets obtained by means of (1) and (2) have been mapped to WN1.5
4.- Those Spanish verbs which mapped to a top-WN1.5 synset (synsets without hypernyms) have been
selected -being rejected the rest
5.- Verbs from this resulting subset (4) which do not occur as genuses in the MRD definitions have
been also removed
6.- Finally, Spanish synsets have been built by collapsing in them those verbs in (5) which mapped to
th the same WN1.5 synset.
Summing up, the process of selection of major concepts for Spanish is the following:
[(a) + (b)] - [c] = subset
[(TOP_WN1.5 & CORPUS_freq_1000+) + (TOPWN1.5 +& MRD_freq_500+)] - [nongenus]
Where:
(a) is the intersection of:
(a.i) the verbs that have a frequency of 1000 ocurrences or over in Lexesp and
(a.ii) the verbs that have translation or mapping to some top verb synset of WN1.5.
(b) is the intersection of:
(b.i) the verbs that have a frequency of 500 ocurrences or over in Vox definitions, and
(b.ii) the verbs that have translation or mapping to some top verb synset of WN1.5.
(c) are the verbs which not occur as genus in Vox
LE2-4003
EuroWordNet
59
EuroWordNet D006: Definition of the links and subsets for verbs
The basic set of major concepts described above must be enlarged in the next phase using at
least the following criteria:
• Adding new verbs appearing as genus terms in definitions of senses in (c) not included in the subset.
• Adding grammatical verbs (modal, aspectual and copulative verbs).
3.1.4 First experiment on verifying intersection among the subsets
For the time being only a first attempt has been carried out to verify intersection among the
subsets. In fact, the final work will be done when the selections are refined and completed.
Thus, we have verified the intersection between the Italian subset on one hand and the Dutch or
Spanish on the other, by searching the same correspondences with WN 1.5 synsets. Italian has been
found to share 37 synsets with Dutch (corresponding to 56 verbs/variants for Italian and 58
verbs/variants for Dutch), and 19 with Spanish (corresponding to 27 Italian variants and 25 Spanish
variants). Further research is thus needed in order to reach a higher overlapping, at least between the
Italian set and the other two. This work is still in progress and can only be completed after more
manual work will have been performed on the base concepts (in particular for Italian). The whole
research will be refined within Task 4.2, when also the semantic links for the first subset of verbs will
have to be built for each language.
3.2 On extending the subset
The identification of a subset of base concepts is only the first step of the definition of the verb
subsets to be encoded within the database. Extension criteria have been identified within D001 and
have been specified in more details by each site.
As far as Italian is concerned, since the wole set of verb definitions within the monolingual
LDB amounts to 14,556 and within the TA it is stated that about 15,000 verbs should be encoded in the
database, all the LDB verb senses should be encoded. However, perhaps the available data will have to
be revised, and, in case it is necessary, somehow enriched, in order to i) improve overlap with other
sites, ii) add most-frequent corpus words not already encoded in the LDB.
In general, the procedure to be followed to extend the subset is the following:
1)
2)
3)
4)
5)
6)
finalize the base concepts
extend the base concepts top-down
extend the base concepts with most-frequent corpus words which are not part of selection yet
extend the base concepts to improve overlap with other sites
add typical Dutch, Italian, and Spanish lexicalizations
add missing Parole words (in order to facilitate the parallel usage of both databases)
LE2-4003
EuroWordNet
60
EuroWordNet D006: Definition of the links and subsets for verbs
7) add terminology (for the addition of terminology we shall consider the terminology that will be
made available in the Interval project and the terminology that is included in the text corpora that will
be used by Novell to verify the EuroWordNet database).
LE2-4003
EuroWordNet
61
EuroWordNet D006: Definition of the links and subsets for verbs
4. Two sample classes encoded: motion verbs and ‘know verbs’
4.1 On coding the relations for two sample classes of verbs
In order to arrive at a final definitions of the relations to be included in our database and to
explore the effectiveness of different approaches, methodologies of analysis and solutions for encoding
them, we performed an experiment, by taking into account two small semantic fields: i) the taxonomy
built starting from the hypernym verbs corresponding to WN 1.5 {move, change position} synset; ii)
‘know’ verbs. The purpose of the experiment was therefore twofold:
•
•
to determine the best approach and methodologies for the different resources
to explicitly define the relations and the coverage of the database.
With regards to the first point, we needed to verify it since the sources that we are going to use
have partially different characteristics and need different methodologies of research. The definition of
the relations, then, needed a preliminary analysis of the kind of data, problems, etc., found both by
working with the available resources and by trying to match each one’s results. It was then crucial that
we agreed at an early stage on the criteria that define the relations and that we anticipated problems and
decisions. (Cf. Section 6. in D005 for a more detailed general discussion of issues connected with the
extraction process and the encoding of relations.)
LE2-4003
EuroWordNet
62
EuroWordNet D006: Definition of the links and subsets for verbs
4.2 First results of work on Dutch
The following verb synsets from WN1.5 were chosen for the experiment:
WN1.5 synsets
verbs
MOVE
<move, change position> (intransitive)
<move, make move, displace> (transitive)
<move, locomote, travel, go> (reflexive)
Dutch equivalents in Vlis
bewegen_1
bewegen_2
bewegen_6
(intr.)
(trans.)
(refl.)
KNOW
<know, cognize>
(be cognizant or aware of a fact
or a specific piece of information;
"I know that the President lied to the people";
"I want to know who is winning the game!";
"I know it's time")
weten_2
The Van Dale database is different from a traditional Machine Readable Dictionary in that some
semantic relations between word senses are already explicitly coded. The Dutch experiment therefore
first of all consists of a critical assessment of the data stored in the Van Dale database Vlis with respect
to the data requirements of EuroWordNet and an investigation of the possibilities to improve this
information using the information extractable from the definitions and the bilingual dictionaries. The
following table shows how the different VLIS relations are distributed for the Dutch equivalents of
“move” and “know” at their next level (for an explanation of the relations see below):
bewegen1
bewegen2
bewegen6
weten2
LE2-4003
SYN
1
1
-
HYPO
21
17
12
2
ANTON
-
ASSOC
1
1
REF
1
1
-
bewegen1
bewegen2
bewegen6
weten2
HYPER
1
1
1
-
CAUSE
-
INCHOA
-
VERB
-
PREF
1
EuroWordNet
63
EuroWordNet D006: Definition of the links and subsets for verbs
If we traverse all relations in downward direction (including all levels, but not considering the
reference relation across parts-of-speech) we get the following figures for the full fields dominated by
these words in VLIS:
bewegen_1
bewegen_2
bewegen_3
weten_2
Related senses
at all levels
145 senses
565 senses
54 senses
24 senses
In the following sections we will discuss these relations and the possible EuroWordNet results for
BEWEGEN (move) and WETEN (know) respectively.
4.2.1. Motion Verbs
The equivalent Dutch senses for the 3 chosen synsets of the verb MOVE are respectively the
senses 1, 2 and 6 of the verb BEWEGEN. In the case of BEWEGEN we see that the sense distinction is
due to the different syntactic patterns:
Sense 1 denotes the intransitive verb: to change place or position (literally translated);
Sense 2 is the transitive verb meaning: to make place or position change (literally translated);
Sense 6 is the reflexive verb having almost the same definition as sense 1 except for the addition "of
people and animals" (literally translated);
It is not clear that they also correspond with relevant semantic differences, e.g. many intransitive
movement verbs can both refer to causative and non-causative movements.
The hyperonyms of these senses also only differ because of the syntactic differences. Both
sense 1 and 6 are related to the intransitive verb VERANDEREN (non-causative change) and sense 2 is
linked to the transitive verb VERANDEREN (causative change):
sense 1 has HYPERONYM veranderen_1 (intransitive) (to become different)
sense 2 has HYPERONYM veranderen_4 (transitive) (to make different)
sense 6 has HYPERONYM veranderen_1 (intransitive) (to become different)
For the transitive and intransitive sense of BEWEGEN these hyperonyms appear to be correct since the
head words of the definitions are the verbs 'veranderen' (to change) and 'doen veranderen' (to make
change). In the case of sense 6, this cannot be correct because a reflexive movement verb mostly
denotes a causative event.
LE2-4003
EuroWordNet
64
EuroWordNet D006: Definition of the links and subsets for verbs
Examination of the hyponyms further illustrates that sense distinction based on syntactic
different patterns is not a clear criterion. The hyponyms of the transitive BEWEGEN are not all
transitives. For example the verb kwispelen_1 (to wag) is a an intransitive verb and it is also encoded
as such in Vlis,. Furthermore there are hyponyms encoded as both transitive and intransitive. Instead of
sense differentiation, these verbs are here only linked to the transitive hyperonym. One of the
hyponyms of the intransitive BEWEGEN is a reflexive verb, namely omdraaien_6 (turn around). Why
it is not a hyponym of the reflexive sense 6 is unclear. The hyponyms of sense 6 represent 10
intransitives verbs, 1 reflexive and 1 transitive/ intransitive verb. Looking further down at the
taxonomy it appears that the correlation between sense distinction and syntactic differences is encoded
even in a less inconsistent way.
If we abandon the syntactic difference we see that three different semantic aspects seem to play
a role for the concept of “movement”:
•
•
•
whether or not something else is being moved
is something moved from A to B or is the position or form of the body or object changed on the
same location
causality of the event, is it controlled by an agent or not.
It is not very clear what the first two aspects really mean. If something is not moving from A to B then
part of the body or object is still being moved from A to B. In many cases it is not so clear what the
difference is. When somebody “jumps” he may do this on the same location or going from A to B, but
is this really relevant for defining “jump”? Something similar can be said for moving oneself and
moving something else. A verb such as “drive” can be used in “He will drive to LA” and in “He will
drive you to LA”, but to what extent do we refer to another type of event?
Another aspect relevant for distinguishing senses is the way these senses would be linked, or
what differences in the relations correspond with these senses. For the first two aspects, we see that
they do not result in a different semantic configuration (i.e. motivate a different semantic relation with
some other verb). This is different for the causality aspect of movement. Since there is a semantic
relation reflecting the difference between a non-causative change and a controlled causative change, we
may consider the differentiation of the concept into two correlating senses as well. The above
syntactically-based senses can then be linked to these semantic senses as follows:
•
causative BEWEGEN, linked to causative VERANDEREN_4 (change) which is transitive. The
next ‘Vlis’ senses are captured by this sense:
intransitive BEWEGEN_1
transitive BEWEGEN_2
reflexive BEWEGEN_6
•
ex. of hyponym: kwispelen (to wag)
ex. of hyponym: wrikken (to wrench)
ex. of synonym: verroeren (to move oneself)
non-causative BEWEGEN, linked to non-causative VERANDEREN_1 (change) which is
intransitive. The next Vlis sense correlates to this sense:
LE2-4003
EuroWordNet
65
EuroWordNet D006: Definition of the links and subsets for verbs
intransitive BEWEGEN_1
ex of hyponym: deinen (to heave, surge)
Next all hyponyms of BEWEGEN (sense 1, 2 and 6) have to be reconsidered in terms of causative and
non-causative movement, where the syntactic categories can be used as a clue. In those cases that a
single sense is labeled as both transitive and intransitive we thus have to decide whether this alternation
correlates with a difference in causation. If so, we have to split the sense in a causative and a
non-causative meaning. Alternatively we can also link it to both causative and non-causative
BEWEGEN using the disjunction operation. In all the cases of such causative alternation we can create
a CAUSE relation from each causative meaning to the non-causative meaning.
From this we can conclude that we have to be aware of over-differentiation of senses due to
syntactic differences and when necessary these differentiations have to be adapted by merging or
splitting senses. Syntactic patterns can nevertheless be used as clues for finding semantic correlations.
4.2.1.1 SYNONYMY
If we look at the explicit synonymy relations we see that the transitive verb BEWEGEN (move)
has a synonym relation with the transitive verb VERROEREN (stir). The reflexive verb BEWEGEN
(move) as well has a synonym relation, namely with the reflexive verb VERROEREN (stir). We have
the following comments on this. First of all the verb “verroeren” is merely a reflexive verb; it is only
used as a transitive in the fixed expression(s): 'geen vin(ger) verroeren' (not move an inch). So we
would suggest not to have this sense at all and therefore there cannot be a synonym relation with it.
Secondly we do believe that the reflexive VERROEREN has a definition that strongly suggests
synonymy with the reflexive BEWEGEN, namely: "zich bewegen" (to move oneself). However the
verb has a strong negative polarity as the example sentences in the bilingual dictionary show:
Verroer je niet
(don’t move)
Je kunt je hier nauwelijks verroeren (you can hardly move hear)
Geen blaadje verroert zich
(not a leaf is stirring)
This is probably due to the fact that it is difficult to substitute BEWEGEN by VERROEREN in many
non-negative contexts, e.g.:
Je kunt je hier goed bewegen/ ?verroeren (One can move oneself well here)
Hij beweegt/?verroert zich voortdurend in zijn slaap (He moves constantly in his sleep)
The question is whether such polarity really affects synonymy. If we decide to join the reflexive sense
of BEWEGEN with the transitive sense, representing a single causative sense, we are faced with the
fact that VERROEREN (which then is synonymous) can only be used as reflexive verb and is mostly
used in negative contexts.
LE2-4003
EuroWordNet
66
EuroWordNet D006: Definition of the links and subsets for verbs
4.2.1.2 HYPONYMY
We already discussed the hyponymy relations for BEWEGEN above.
4.2.1.3 ANTONYMY
In total 148 items for verbs have an antonymy relation. Directly at the level of BEWEGEN we
do not find any examples either but, indirectly, we do find an example “stilhouden” (to keep still):
stilhouden 1 {to keep still}
SYNONYM stoppen 1 {to stop moving}
ANTONYM voortgaan 1 (to continue)
HYPERONYM gaan 3 (to go)
ASSOCIATIVE komen 1 (to come)
ANTONYM uitblijven 1 LEAF (to not happen)
Here we see that “stilhouden” (to keep still) is a synonym of “stoppen” (to stop) which has an
antonymy relation with “voortgaan” (to continue). In its turn “voortgaan” is linked to “gaan” (go)
which should have been linked as a hyponym of BEWEGEN but for some reason is not (see below).
Further down we see that “komen” (to come) also has an antonym “uitblijven” (not to happen).
However, this is beyond the level of BEWEGEN.
At more specific levels (as far as we can collect them now) there are only 8 senses with an
antonym link (out of 764 senses linked to the three senses of BEWEGEN at any level):
centraliseren 1
(centralize)
=antonym=
decentraliserem 1
(decentralize)
leegschenken 1
=antonym=
(to empty by pouring)
volschenken 1
(to fill by pouring)
neergooien 1
(to throw down)
opgooien 1
(to throw upwards)
=antonym=
verkrampen 1
=antonym=
(to seize with cramp)
ontspannen 7
(to relax)
verspreiden 1
(to spread out)
verzamelen 2
(to collect)
LE2-4003
=antonym=
EuroWordNet
67
EuroWordNet D006: Definition of the links and subsets for verbs
We expect that many more movements reflect some opposition but these are either not related
as such or are linked to hyperonyms which are not related to BEWEGEN (for some reason). In the case
of “dalen” (descent) and “stijgen” (rise) we see that there is an odd hyperonym toenemen (increase) for
“stijgen” which is not further related:
dalen 1 (to descent)
ANTONYM=> stijgen 3 (to rise)
HYPERONYM=> toenemen 1 (increase)
Upward LEAF
ASSOCIATIVE => vallen 2 (fall) Upward LEAF
In the case of “weggaan” (leave) and “arriveren” (arrive) we see that they are both linked to an
alternative hyperonym “gaan” (to go). However, in this case no antonymy relation is encode in Vlis:
weggaan 1 (to leave)
SYNONYM => vertrekken 1 (to leave)
HYPERONYM=> gaan 3 (to go)
HYPERONYM=> komen 1 (to come)
arriveren 1 (to arrive)
SYNONYM => aankomen 1 (to arrive)
HYPERONYM=> komen 1 (to come)
The missing antonymy relations have to be find using information from the definitions.
4.2.1.4 ASSOCIATIVE
The ASSOCIATIVE relation is used in Vlis when the hyperonym relation was not satisfying or
when there seemed to be a strong association with another entry which could not be expressed
otherwise. This relation can be cross-part-of-speech. Within the Vlis database the relation has been
assigned 557 times to a verb. Within the scope of the experiment we found occurrences of an
associative relation for the verb bewegen_6 (to move), namely voortbewegen_2 (to move on/ forward)
.
The associative voortbewegen_2 with the reflexive BEWEGEN is a reflexive itself and has no
definition. The translation ‘move on/forward’ already illustrates that the verb expresses movement in a
certain direction. This observation is also confirmed by the hyponyms of ‘voortbewegen_2’ (to move
forward) which all more or less express a movement forward, e.g.: to skate, swim, glide, walk, with
exception of the hyponym ‘to jump’. The hyponyms of the reflexive BEWEGEN express movement
of which the direction is not specified, e.g.: to dance, to swarm. Just as we have seen with “weten 2”
(to know) and its ASSOCIATIVE “begrijpen 1” (to understand) it seems to be the case that the
associative relation actually denotes a hyponym relation. So voortbewegen_2 (move on) is a hyponym
of bewegen_6 (move) based on the translation and the linked hyponyms.
LE2-4003
EuroWordNet
68
EuroWordNet D006: Definition of the links and subsets for verbs
Another interesting relation is the hyperonym of ‘voortbewegen_2’ (to move on) which is
gaan_3 (to go). The definition of this verb is: to move oneself and by that change place (translated
literally). This definition looks very similar to the definition of bewegen_1: to change place or position
(literally translated). The only difference is that gaan_3 (to go) cannot apply to position but just to
place, which makes it more specific then BEWEGEN (to move). However the verb gaan_3 (to go) is a
top node in Vlis and is not related to the verb BEWEGEN (to move). To clarify the situation we show
the relevant links in Vlis as they were and the new situation proposed:
Old situation:
veranderen (to change)
gaan (to go)
|HYP |HYP
|HYP
bewegen
zich bewegen ASSOCIATIVE ‡ zich voortbewegen (to move on)
(move)
(to move oneself)
(to move oneself on)
New links:
veranderen (to change)
|HYP
bewegen (to move)
|HYP |HYP
zich voortbewegen gaan
(move on)
(to go)
The question is whether voortbewegen (to move on) is a hyponym of gaan (to go) or that we should
talk of a sort of synonym relation. If the synonymy relations does fully apply it is also possible to
create a NEAR_SYNONYM link between “gaan” (go) and “voortbewegen” (move forward) to make
clear that these two co-hyponyms of “bewegen” (move) are close in meaning, as opposed to all the
other co-hyponyms we may find below “bewegen” (move). Clear tests for synonymy and hyponymy
for verbs are required here.
CONCLUSION
We looked at other examples for associative relations and came to the conclusion that in most
cases the assigned relation can be converted to another relevant relation, which helps to improve the
taxonomy.
4.2.1.5 REFERENCE
In VLIS this relation denotes morphological relationships between all categories and it has been
assigned 838 times to verbs. Within the subset of the experiment only reference relations for verbs
LE2-4003
EuroWordNet
69
EuroWordNet D006: Definition of the links and subsets for verbs
were found. Bewegen_1 (move) has a reference link to the noun 'beweging' (movement), bewegen_2
(move) has a reference link to the adjective 'beweegbaar' (movable). The relation is not assigned very
consistently; since both verbs should have a reference link to the noun and adjective.
The link with the adjective cannot be expressed semantically in EuroWordNet. There is no
relation for acts and adjectives denoting capabilities to perform these acts. In the case of the
verb-to-noun relation, “bewegen-beweging”, this can be expressed as a noun-to-verb synonymy
relation in EuroWordNet. However, this is not always the case. A verb such as “aanplanten” (to plant
out) has a reference relation to “aanplant” (the planting) which is not the event but the object of to plant
(that what is undergoing the event). This has to be converted to a PATIENT_ROLE relation between
the verb and the noun. All the reference relations thus have to be checked for this difference in
interpretation. For this the information from the definitions will be very helpful.
4.2.1.6 CAUSATIVE
In Vlis this relation has rarely been assigned; only 5 times in total.
VlisSense neerlaten_1 def = "naar beneden laten, laten zakken"
=> VlisSense zakken_1 def = "dalen, naar beneden gaan"
(let down)
(go down)
VlisSense onderwerpen_2 def = "een behandeling doen ondergaan" (put through)
=> VlisSense ondergaan_1 def = "doorstaan, verduren"
(go through)
VlisSense opmaken_1 def = "geheel opgebruiken"
=> VlisSense opraken_1 def = none
LE2-4003
(use up)
(run out)
EuroWordNet
70
EuroWordNet D006: Definition of the links and subsets for verbs
VlisSense tegenmaken_1 def = "maken dat iem. tegenzin in iets krijgt, ergens tegen
is"
(make unwanted)
=> VlisSense tegenstaan_1 def = "onaangenaam zijn, weerstand oproepen"
(pall upon)
VlisSense uitdoen_1 def = "uittrekken, afleggen"
(take off)
= > VlisSense uitgaan_7 def = "uitgetrokken kunnen worden" (come off)
These relations all seem to be correct but in many cases causation information has been left out and
thus has to be derived differently.
4.2.1.7 INCHOATIVE
Another relation that occurs in VLIS but not in WN1.5 is the inchoative relation. According to
the guidelines for Vlis this relation is used to encode that a verb marks the beginning of another action.
In practice, however, it turns out to be used to relate verbs that cause a non-dynamic situation referred
to by another verb:
krijgen1 def = ontvangen (to get)
=> hebben def. = bezitten (to have)
kamperen1 def ="ergens een tijdelijk verblijf buitenshuis opslaan, vooral in
tent of caravan" (to camp)
=> legeren def = "zijn legerplaats opslaan" (to encamp)
"dichtzitten1" def = "afgesloten zijn" (to be closed)
=> dichtgaan1 def = "gesloten worden" (to close)
These relations have to be reinterpreted as CAUSES relations between verbs in EuroWordNet.
4.2.1.8 VERB
This relation is specifically used in VLIS to link compound verbs to their base form, which
highly occur in Dutch (used 723 times in the database). In many cases, however, the relation between
the base and the compound form is not clear. For example the verb gaan_3 (to go) has 12 verb
relations, among which:
afgaan_2
uitgaan_9
overgaan_8
LE2-4003
def. = helemaal langsgaan
def. = ten einde gaan
def. = overschrijden
(go along the line)
(go to the end)
(go over a line)
EuroWordNet
71
EuroWordNet D006: Definition of the links and subsets for verbs
In the first two examples the prefix ‘af-‘ and ‘uit-‘ denote resp. the concept of ‘all along’ ‘to the end’.
Verbs can be combined with these prefixes quite productively: aflopen (walk along),afwerken (work
along), afzoeken (search along), affietsen (cycle along); uitlopen (walk to the end), uitfietsen (cycle to
the end). The prefix ‘uit-‘ is combined with movement verbs and the ‘to the end’ concept is the literal
meaning. But as the examples show the prefix ‘af-‘ also combines with verbs denoting other sorts of
actions/ processes, in which case the ‘all along’ concept is meant figuratively. There are other prefixes
that have this property as well, e.g.:‘ door- ‘ (through): doorlopen (walk through), doorzoeken (search
through) .
The last case of ‘overgaan_8’ (cross) we are not dealing wit a new word form but with a wrong word
form which does not occur in this meaning. In this meaning the preposition “over” should be separate
from the base form and is productively combined with “gaan” (over) in its normal meaning. Two other
examples of over-generated senses are:
aanschoppen_1 definition = "een schop geven tegen" (to kick against)
=> schoppen_2 = een schop geven (to kick).
aanzwellen 1 definition = "in omvang of sterkte toenemen" (swell, rise)
=> zwellen_2 = (van geluiden) geleidelijk sterker worden (swell, rise)
In this last example “aanzwellen” is linked to “zwellen” which has a definition which fits “aanzwellen”
but not “zwellen”. So the senses of ‘overgaan’, ‘aanschoppen’ and “zwellen 2” have to be omitted from
the database.
From this it follows that the VERB relations cannot systematically be interpreted in terms of a
single EuroWordNet relation and they all have to be reinterpreted on the basis of other information.
4.2.1.9 PREFERENCE
The PREFERENCE relation is used to refer to other entries which are preferred and where the
relevant information should be found. For verbs it occurs 637 times, e.g.:
aanbotsen_1 preference true is
(bump against)
botsen
(bump)
aanliggen_2 preference true
(lie close to)
liggen
(lie)
aanbranden_1 preference false is
(burn on)
aangebrand
(burnt (on))
aantekenen_1 preference false
aangetekend
LE2-4003
EuroWordNet
72
EuroWordNet D006: Definition of the links and subsets for verbs
(make an note of)
(written down)
There are many past participle forms of verbs that have special meanings, but which are
nevertheless linked to their infinitive verb form. We also see some compound verb forms. For each of
these case the relevant relations have to be extracted from other information. As such the relation is not
useful.
4.2.1.10. Information in definitions
We inspected the definitions for generating (missing) relations and improving less reliable
relations in Vlis. In order to find these cases we inspected all senses with definitions in which
inflections of “bewegen” and other keywords occur like: 'beweging' (movement), 'heen en weer
bewegen’ (move/go back and forth), “gaan” (go).
A search on the last keyword resulted in 24 items of which 9 are related as hyponyms of
'bewegen' (move) already. The remaining 15 items are potential hyponyms of 'bewegen' (move), but for
some reason other relations are assigned. In many cases these candidates were assigned as synonyms of
one of the hyponyms of BEWEGEN (move), so in fact these are hyponyms too. In other cases the
candidates were related to another hyperonym, e.g.:
zwaaien_3 trans_intr
(to wave)
"(iets) krachtig heen en weer bewegen"
(to move something back and forth with power)
HYPERONYM = groeten_1 (to greet)
differentiae = "met de hand" (with the hand)
The verb 'zwaaien_3' (to wave) is a hyponym of 'groeten_1' (to greet) which is an obscure assignment.
The information that 'zwaaien_3' (to wave) sometimes is a sort of greeting is not in the definition,
besides it is too specific and should therefore not be its (only) hyperonym. It should be a hyponym of
'bewegen' (move), which could have been found by its definition.
likken_1 trans_intrans
met de tong heen en weer gaan over
(to lick)
(move with the tongue back and forth over)
HYPERONYM = aanraken_1
(to touch)
differentiae = "met een heen en weer bewegende tong"
(with a back and forth moving tongue)
The verb 'likken_1' (to lick) is a hyponym of 'aanraken_1' (to touch). Which is not wrong, but it is not
the only more generic term that applies; it could also be a sort of cleaning. Again the information in the
definition is overruled by other non-explicit information.
To extract synonyms we can use the following resources:
LE2-4003
EuroWordNet
73
EuroWordNet D006: Definition of the links and subsets for verbs
_
_
_
bilinguals: all words which have the same translation(s)
monolinguals: all words with exactly the same definition, explicit synonymy relations
WordNet1.5: translation of variants in the synsets
For extracting synonyms from translations we use an Acquilex-tool which generates all possible
English translations of a set of Dutch source words in the bilingual Dutch-English dictionary (Martin
and Tops, 1986) and next looks up these English words in the bilingual English-Dutch dictionary to see
what Dutch translations are generated. In those cases that one of the Dutch source words was found
among the translations of the English word the translations have been proposed as possible candidate
for translation. These cases form a so-called translation-circle: we return back to one of the source
words via the bilingual dictionaries. If these Dutch translation of the English translation contains new
words these are offered as potential synonyms of the Dutch source words. In the following lists we find
the result of this process starting with “bewegen” (move):
Following startword has been selected:
((bewegen 7112 (1) V) (bewegen 7113 (1 2) V) (bewegen 7114 (1) V))
Currently Processing item:
(bewegen 7114 (1) V)
Translation Circles:
(budge (zich_roeren zich_ver_roeren zich_verplaatsen bewegen zich_bewegen))
(travel (lopen heen_en_weer_lopengaan bewegen zich_bewegen verschuiven))
(run
(voortbewegen
bewegen
schuiven
glijden
rollen
rijden
hard_rijden
heen_en_weer_rijdenvaren pendelen aflopen voorbijgaan draaien werken sijpelen
druipen stromen weg_stromen uitlopen trekken zwemmen klimmen kruipen
rondzwerven weiden gelden v._kracht_zijn zich_uitstrekken gaan lopen voortduren
duren voortgaan))
(start (bewegen plotseling_bewegen losspringen aanslaan te_voorschijn_springen))
(stir
(bewegen
in_beweging_brengen
roeren
ontstellen
verontrusten
beroeren
wakker_maken))
(stir
(roeren zich_ver_roeren bewegen zich_bewegen))
(move (bewegen in_beweging_zetten in_bewegingberoering_brengen roeren ver_roeren))
(move (drijven aansporen bewegen aanzetten ertoe_zetten))
(move (bewegen
zich_bewegen
in_beweging_komen
zich_in_beweging_zetten
v._positiehouding_veranderen zich_verplaatsen))
Acceptable Equivalents from the translation Circles:
(roeren 61572 (1) V)
(roeren 61570 (2) V)
(voortgaan 81599 (1) V)
(gaan 20427 (1 2 3 4 5 6 7 8) V)
(voortbewegen 81584 (1) V)
LE2-4003
EuroWordNet
74
EuroWordNet D006: Definition of the links and subsets for verbs
Each of the translation-circles consists of the English word followed by a list of the translations
and translation phrases. Below the translation-circles a list of the equivalents which have been accepted
(this can be determined using an interactive inter-face). All the other translations either could be found
in the dictionary or are not acceptable (refer to another sense of “bewegen”). This strategy resulted in
the new synonyms “roeren” and “voortgaan” and in “gaan” and “voortgaan” which are linked as
associatives in Vlis.
In the case of the monolingual resources we looked at words with the same definitions and for
explicit synonymy links encoded in another Dutch dictionary (van Sterkenburg and Pijnenburg, 1984).
The explicit synonym fields in Vlis only give “verroeren” (move) as a synonym which is also given as
the only synonym in Vlis. An example of similar definitions within the movement field is the following
pair:
Examples of words with (exactly) the same definition
beven_1 (intransitive)
def. = met een korte snelle beweging heen en weer gaan
(shake)
(go back and forth wit a short quick movement)
trillen_1 (intransitive)
def. = zich snel heen en weer bewegen
(tremble)
(to move oneself quickly back and forth)
The distributions of the verbs beven_1 (shake) and trillen_1 (tremble) are very similar as well:
•
•
•
de grond beeft/trilt onder zijn voeten
(the ground shakes/ trembles under his feet)
zijn handen trillen/beven
(his hands are shaking/trembling)
zijn ooglid trilt/beeft
(his eyelid trembles)
Not in all cases the verbs can be interchanged, e.g. not in the expression : met schrik en beven. (with
fear and tremble). Nvertheless, in Vlis trillen_1 is a hyponym of 'beven_1' whereas it seems more a
(near) synonym.
A final method for finding new synonyms is by translating variants of the equivalent
WordNet1.5 synsets:
<move, make move, displace> (transitive)
<move, locomote, travel, go> (reflexive)
The first synset shows that ‘displace’ is a variant of move and make move. The translation of this
variant is the Dutch verb ‘verplaatsen’, which has the definition: ‘elders plaatsen’ (place somewhere
else). In Vlis this verb is a hyponym of ‘brengen’ (to bring) and it is not related to “bewegen” (move).
The strategy therefore does not result in a new synonym but it does create a connection with hyponyms
LE2-4003
EuroWordNet
75
EuroWordNet D006: Definition of the links and subsets for verbs
that are not inter-linked in Vlis. The second synset results in “reizen” (travel) and “gaan” (go) as new
candidates, where “reizen” is a hyponym of “voortbewegen” (move forward) in Vlis. As mentioned
above “gaan” can also be seen as a hyponym of “voortbewegen” which suggests that “gaan” (go) and
“reizen” (travel) are potential synonyms linked together as a hyponym-synset of “voortbewegen”
(move forward). However, they hardly share the same distribution, since “reizen” (travel) is only used
for longer trips and “gaan” (go) can be used in a much more general way: i.e. “reizen” (travel) should
be a hyponym of “gaan” (go).
As we have seen above other relations than hyponymy and synonymy are hardly present in the
Vlis database and we therefore fully have to rely on the information extractable from the definitions.
The next examples illustrate typical patterns found in the experiment subset BEWEGEN (move).
Using relatively-simple techniques we have found many antonyms within the domain of
movement verbs. The first strategy involved scanning the definitions with “bewegen” (move), “gaan”
(go), “voortbewegen” (move forward) for the negation operator “niet” (does not). This is illustrated by
the following examples:
buitensluiten: 1
(to lock out)
Relations:
buiten een afgesloten plaats houden , niet binnenlaten
(to keep outside a locked place, not let in)
buitensluiten: 1
ANTONYM binnenlaten (let in)
eindigen: 1
(to end)
Relations:
niet verder gaan
(not on go)
eindigen: 1
staan: 8
niet bewegen
(stand)
(not move)
Relations:
staan: 1
NEAR_ANTONYM gaan (go)
NEAR_ANTONYM bewegen (move)
wegblijven: 1
(stay away)
Relations:
niet komen waar men verwacht wordt of hoort te komen
(not come where one is expected or should come)
wegblijven: 1
ANTONYM komen (come)
missen: 2
(miss)
Relations:
niet treffen , niet raken
(not hit)
missen:2
ANTONYM raken (hit)
Not all these cases also result in a direct antonymy relation. In some cases the opposing event
expressed in the definition is not a lexicalized word and therefore we cannot express the antonymy
relation:
opblijven: 1
(stay awake)
binnenhouden: 1
LE2-4003
niet naar bed gaan
(not to bed go)
binnenshuis houden , niet uit laten gaan
EuroWordNet
76
EuroWordNet D006: Definition of the links and subsets for verbs
(to keep in)
(insight keep, not let go out)
The verb “gaan” (go) is much too general to be an antonym of “opblijven” (stay awake) or
“binnenhouden” (to keep in). What is possible is to link these words as hyponyms of a direct antonym
of “gaan” (go). However, such an antonym does not exist either. Possible candidates such as
“stilliggen” (to lay still), “stilstaan” (to stand still), “stilzitten” (to sit still) are too specific. The least
thing we can do then is to link it higher up the hierarchy to the antonym of “veranderen” (change),
which is “toestand” (state).
Other negative elements in definitions are e.g. “voorkomen” (prevent), “onmogelijk maken”
(make impossible):
ontwijken: 1
(to move aside)
Relations:
voor iets of iem. uit de weg gaan , om een botsing te voorkomen
(to move aside for something or someone, to prevent a clash)
ontwijken: 1
NEAR_ANTONYM botsen (clash)
blokkeren: 4
(to block)
Relations:
de beweging onmogelijk maken van
to make movement impossible
blokkeren:4
NEAR_ANTONYM beweging (movement) Noun
Antonyms can also be encoded in a more implicit way. Many verbs refer to a change of state.
Those verbs that refer to opposing end-results can be seen as a kind of antonyms as well. Consider for
example two large classes of movement verbs that are in an opposition relation: fill-events and
empty-events. There are many verbs that result in an empty or full state where the manner of filling or
emptying is specified. In Dutch many of these verbs are in fact compounds where the verb denoting the
event is combined with the resulting adjective “vol” (full) and “leeg” (empty). These can be found
easily in Dutch on the basis of their composite structure:
LE2-4003
EuroWordNet
77
EuroWordNet D006: Definition of the links and subsets for verbs
leegmaken: 1 to empty
leegstorten: 1 to empty by dropping
leeggooien: 1 to empty by throwing out
leegstromen: 1 to empty by streaming
leeglopen: 1 to empty by streaming
leegbloeden: 1 to loose one’s blood
leeggieten: 1 to empty by pouring
leegschenken: 1 to empty by pouring
leeghalen: 1 to empty by taking out
leegpompen: 1 to empty by pumping
leegdrinken: 1 to empty by drinking
leegeten: 1 to empty by eating
leegplunderen: 1 to empty by plundering
leegroven: 1 to empty by robbing
leegruimen: 1 to empty by cleaning
leegscheppen: 1 to empty by digging
leegschudden: 1 to empty by shaking
leegstelen: 1 to empty by stealing
leegzuigen: 1 to empty by sucking
vullen: 1 to fill
volstorten: 1 to fill by dropping
volplempen: 1 to fill by dropping in an
uncontrolled way
volstromen: 1 to fill by streaming
vollopen: ? to fill by streaming
volgieten: ? to fill by pouring
volschenken: 1 to fill by pouring
volstoppen: 1 to fill completely
volproppen: 1 to fill completely by pushing
volpompen: 1 to fill using a pump
volscheppen: ? to fill by digging
Not every event has also an opposing event. It is simply impossible to fill by stealing, robbing,
sucking, eating or drinking, etc.. The words followed by question marks are not in the database (for
some reason).
A final special case of negation is the following example in which we do not find a full
opposition but in which the caused effect is negated:
misschieten: 1
(to miss when shooting)
bij het schieten niet raken
(during shooting not hitting)
We cannot say that “misschieten” (to miss when shooting) is an antonym of “schieten” (shoot)
because it implies it. We cannot say either that it is an antonym of “raken” (hit) because it is much
more specific. The information in the definition matches that of a cause relation. The best solution
would therefore be to store the information as a cause relation where the result or caused event is
negated:
Relations
LE2-4003
misschieten: 1 CAUSES
NOT raken
EuroWordNet
78
EuroWordNet D006: Definition of the links and subsets for verbs
The negation label NOT would likewise function in the same way as factive and non-factive
implications. Note that we can store the above verbs with a contrastive result-state in exactly the same
way using this operator:
leegpompen
(to empty by pumping)
HAS_HYPERONYM
CAUSES
volpompen
(to fill by pumping)
HAS_HYPERONYM
CAUSES
pompen
(pump)
leeg (empty)
pompen
(pump)
NOT leeg (empty)
In this way we can differentiate the antonymy relation in terms of an opposition in result and
not as a full opposition of the events. It is also clear now that the manner of getting at the result is the
same for both verbs. A negation operator would provide a very powerful and useful way of encoding
all kinds of contrasts and implications in the database.
The following examples illustrate definition patterns for CAUSE relations that have been found
for movement verbs:
_
doen (do) + Vinfinitive
brengen: 3
(bring)
Relations:
<iets> doen toekomen
(<something> do get)
brengen: 3
CAUSES
toekomen (get)
The following example expresses a causation relation between verbs but the resulting verb does not
refer to a dynamic event or process but to a state.:
zetten: 1
(put)
Relations:
rechtop op de genoemde plaats doen zitten, rusten, staan
(upright on the named place do sit, rest, stand)
zetten: 1
CAUSES
zitten d1 (sit)
zetten: 1
CAUSES
rusten d2 (rest)
zetten: 1
CAUSES
staan d3 (stand)
Note also that the coordination is interpreted here as a disjunction of relations expressed by the
disjunction indexes d1, d2, d3.
LE2-4003
EuroWordNet
79
EuroWordNet D006: Definition of the links and subsets for verbs
_
laten (let) + Vinfinitive
druppelen: 2
(drip)
Relations:
_
om te (in order to)
sjorren: 1
Relations:
_
•
druppels laten vallen
(drops let fall)
druppelen: 2
CAUSES
vallen (fall)
ingespannen aan iets trekken om het te verplaatsen of om het los of
vast te maken
(to pull fiercefully in order to move it or in order to untie it or tie it)
sjorren:1
HAS_HYPERONYM trekken (pull)
sjorren:1
CAUSES/HAS_HYPERONYM
verplaatsen (move)
sjorren:1
CAUSES
los d1 (free, untied)
sjorren: 1
CAUSES
vast d2 (tied)
door (te) (by means of)
aftrekken: 5
(tear off)
Relations:
dichttrekken:2
Relations:
wegkappen: 1
Relations:
verwijderen door te trekken
(remove by pulling)
aftrekken:5
CAUSES/HAS_HYPERONYM
verwijderen (remove)
aftrekken:5
HAS_HYPERONYM trekken (pull)
sluiten door te trekken
(close by pulling)
dichttrekken:2 CAUSES/ HAS_HYPERONYM
sluiten (close)
dichttrekken:2 HAS_HYPERONYM trekken (pull)
<iets> verwijderen door te kappen
(<something> remove by cutting)
wegkappen:1 CAUSES/ HAS_HYPERONYM
verwijderen (remove)
wegkappen:1 HAS_HYPERONYM kappen (cut)
Note that in most of these examples the events are not disjoint in time. Since they also fit the
hyponymy test the HAS_HYPONYM relation should be preferred here above the CAUSES relation.
LE2-4003
EuroWordNet
80
EuroWordNet D006: Definition of the links and subsets for verbs
The next examples illustrate SUBEVENT relations:
_ temporal relations between events expressed by prepositions such as “onder” (under),
“gedurende” (during), “terwijl” (while), “tijdens (during).
ontvoeren: 1
(kidnap)
Relations:
<iem.> onder dwang meenemen
(<somebody> under pressure take away)
ontvoeren:1 HAS_HYPERONYM
meenemen(take
ontvoeren:1
dwang (pressure)
away)
trekkebenen:
(limb)
Relations:
SUBEVENT
met een been trekken tijdens het lopen
(to pull your leg during walking)
trekkebenen:1 HAS_HYPERONYM
trekkebenen:1 SUBEVENT
trekken (pull)
lopen (walk)
_ conjunctive coordination, V en (and) V.
aanhouden: 9
Relations:
arresteren en voor verhoor meenemen
(arrest and take away for interrogation)
aanhouden:9 SUBEVENT
aanhouden:9 SUBEVENT
arresteren (arrest)
meenemen (take with)
_ met (with) higher-order noun + V
fladderen: 1
Relations:
LE2-4003
met ongelijkmatige bewegingen vliegen
(with irregular movements fly)
fladderen
HAS_HYPERONYM
fladderen
SUBEVENT
vliegen (fly)
beweging N
(movement)
EuroWordNet
81
EuroWordNet D006: Definition of the links and subsets for verbs
The following examples illustrate the cross-part-of-speech relations that can be extracted:
_ doen (do), geven (give), beoefenen (perform), zijn (be) + higher-order noun
slaan: 1
(hit)
Relations:
veroorzaken
(to cause)
Relations:
een slag of slagen geven
(a blow or blows give)
slag N
XPOS_HAS_SYNONYM
(blow)
oorzaak zijn van
(cause be of)
oorzaak N
ROLE_AGENT
(a cause)
slaan (hit) V
veroorzaken V
(to cause)
_ V + met first-order-noun
carpoolen:1
(carpool)
Relations:
gezamelijk gebruik maken van een auto voor het woon-verkeer
(common use of a car for home-travelling)
carpoolen V INVOLVED_INSTRUMENT auto N
(carpool)
(car)
_ Adjective maken (make), worden (become)
vullen: 1
(fill)
Relations:
vol maken
(full make)
vullen: 1
CAUSES
vol (full) Adj
These examples are by no means complete. They only illustrate the patterns in the definitions
that can be used to extract these relations. As general conclusion it shows that a lot of additional
information can be extracted from the resources using semi-automatic techniques.
4.2.2. Verbs of knowing
The Dutch equivalent for “know” is “weten” which has 5 senses in Vlis, one as a noun and 4 as
a verb:
SENSE 1. NOUN.
kennis, wetenschap omtrent iets
(knowledge of something)
SENSE 2. VERB_transitive kennis hebben van
(to have knowledge of)
SENSE 3. VERB_transitive inzien, begrijpen
(to see, understand)
LE2-4003
EuroWordNet
82
EuroWordNet D006: Definition of the links and subsets for verbs
SENSE 4. VERB_transitive zich bewust zijn
(to be conscious of)
SENSE 5. VERB_transitive kans zien, erin slagen, in staat zijn
(to manage, succeed, be capable of)
The first sense refers to the noun “weten” which is equivalent to the second sense of “weten”.
Remarkably, this is not expressed by a so-called reference relation in Vlis which is used to mark such
morphological correspondences. The 5th sense is clearly different and not relevant for our discussion
here, the other senses are closely related to English KNOW.
All the verbal senses of “weten” are coded as transitive so we cannot say there is
over-differentiation due to different syntactic patterns (although it is not clear to what extent further
differences in the complementation have triggered the senses distinction). From a semantic point of
view we can say that the second sense of WETEN is clearly the most neutral and basic sense. This is
confirmed by the Vlis data:
•
•
WETEN_2 is a top or upper-leaf (it has no more general hyperonyms or other more general
concepts to which it is related).
the other verbal senses are directly or indirectly related to sense 2.
The latter point is illustrated by taking the Vlis relations of sense 3 and 4 in upward direction:
weten 4 (know)
SYNONYM realiseren 2 (realize)
HYPERONYM weten 2 LEAF (know)
weten 3
SYNONYM begrijpen 1 (understand)
ASSOCIATIVE weten 2 LEAF (know)
Here we see that “weten 4” is a synonym of “realiseren 2” which in its turn is a hyponym of “weten 2”,
and “weten 3” is a synonym of “realiseren 2” which has an association relation with “weten 2”.
The neutral, basic sense of “weten 2” is correctly defined as “to have knowledge of”. The more
special 3rd sense of “weten” refers to a special kind of knowledge: the solution to a problem, the
structure of a complex condition, the working of a mechanism. It is not clear what is meant with the
association relation here. It could have been linked as a SYNONYM or a HYPONYM of “weten 2”.
The more special 4th sense refers to conscious knowledge as opposed to unconscious knowledge. It is
not clear whether these more special meanings can be seen as pragmatic restrictions in the usage of
“weten” or that they really denote distinct meanings. If we look at more coarse distinctions we see
that none of the senses refers to an action or process: they all refer to the state of knowing something. It
is also possible to explain the 3rd and 4th senses of “weten” by means of a pragmatic principle that a
general word can often be used to refer more specific concepts as well.
LE2-4003
EuroWordNet
83
EuroWordNet D006: Definition of the links and subsets for verbs
4.2.2.1 SYNONYMY
As suggested above there are no synonyms for the abstract meaning of “weten 2”. The more specific
meanings will be discussed below.
4.2.2.2 HYPERONYMY
There are no hyperonyms for the abstract meaning of “weten 2” which is reasonable (see
above), while the 3rd and 4th meaning are indirectly linked to the 2nd sense. In the case of the 3rd
meaning we already mentioned that its synonym “begrijpen 1” should have been linked to “weten 2” as
a hyponym rather than the unspecific relation association.
4.2.2.3. HYPONYMY
In the case of the verb weten_2 (to know) we see that there are two explicit hyponyms in VLIS and one
associative “begrijpen 1” which should be seen as a hyponym:
kennen_2
(to know)
door studie of oefening geleerd hebben
(learned by study or training)
realiseren_2
(realize)
beseffen
(be aware of)
begrijpen_1
(to understand)
de samenhang doorzien van
(to see the coherence of)
The last two have been explained above. The hyponym “kennen_2” refers to learned facts which is
again a special subsets of facts in general. If we reinterpret the association relation as hyponymy we
thus would get the following general picture for WETEN in Dutch, where “weten 3” and “weten 4”
pop up as hyponyms of “weten 2”:
LE2-4003
EuroWordNet
84
EuroWordNet D006: Definition of the links and subsets for verbs
weten 2 {to have knowledge of}
HYPONYM begrijpen 1 {knowledge of complex facts}
(previously ASSOCIATIVE)
SYNONYM bevatten 2 LEAF
SYNONYM doorhebben 1 LEAF
SYNONYM snappen 3 LEAF
SYNONYM vatten 2 LEAF
SYNONYM verstaan 2 LEAF
SYNONYM weten 3 LEAF
HYPONYM aanvoelen 2 {to know on intuition, without factual evidence}
SYNONYM invoelen 1 LEAF
SYNONYM voelen 6 LEAF
HYPONYM voorvoelen 1 LEAF (to know in advance}
HYPONYM doorzien 1 {to know something very complex}
HYPONYM doorgronden 1 {to know something extremely complex}
SYNONYM doorvorsen 1 LEAF
HYPONYM misvatten 1 {to wrongly understand}
SYNONYM misverstaan 1 LEAF
HYPONYM kennen 2
HYPONYM realiseren 2
SYNONYM beseffen 1
SYNONYM inzien 2 LEAF
SYNONYM weten 4 LEAF
HYPONYM onderkennen 1 LEAF {to know despite one’s wishes}
This hierarchy is acceptable accept for “misvatten” which is not a hyponym but an antonym of
“begrijpen”.
4.2.2.4 ANTONYMY
For WETEN (know) we do not find any cases of explicitly coded antonymy. We have already
seen in the previous section that “misvatten” should have been coded as an antonym of the hyponym
“begrijpen” (to know something complex) but was coded as a hyponym.
4.2.2.5 ASSOCIATIVE
The association relations for WETEN (know) have been discussed above. In addition we also
find an association relation between “realiseren 2” (realize) and “begrijpen 1” (understand) without
really adding any information.
LE2-4003
EuroWordNet
85
EuroWordNet D006: Definition of the links and subsets for verbs
4.2.2.6 REFERENCE
We already suggested that the REFERENCE relation of “weten 1” (as a noun) and “weten 2”
was missing. Other REFERENCE relations are hardly present in this domain.
4.2.2.7 CAUSATIVE
There is no causative relation encoded for senses related to WETEN in Vlis.
4.2.2.8 INCHOATIVE
In the case of WETEN there is one INCHOATIVE relation with the hyponym “begrijpen”
(understand):
begrijpen 1 (understand)
INCHOATIVE=> doorkrijgen 1(to get to understand)
The verb “doorkrijgen” refers to the event in which a person undergoes a change of mental state from
not-understanding to understanding.
4.2.2.9 VERB and PREFERENCE
Verb and preference relations have not been encoded in Vlis for verbs related to WETEN.
4.2.2.10. Information in definitions
As we have seen above, WETEN (know) denotes a process which is not as differentiated as the
concept of MOVEMENT. By looking at definitions in which the verbs “weten” (know), “begrijpen”
(understand) and “realiseren” (realize) occur we tried to find more relations which have not been
encoded in Vlis. The potential field of know-verbs has been enlarged by looking also at definitions in
which the noun “kennis” (knowledge) occurs.
This strategy did not give many new results with respect to hyponymy relations. The following
list contains the concepts that come closest to WETEN:
LE2-4003
EuroWordNet
86
EuroWordNet D006: Definition of the links and subsets for verbs
(herinneren 33581332 (2) V)
(ignoreren 16806482 (1) V)
(impliceren 16806741 (1) V)
(inzien 16808261 (1) V)
(kennen 16809949 (1 4) V)
(misvatten 16821041 (1) V)
(misverstaan 16821043 (1) V)
(onderkennen 16825609 (1) V)
(ontkennen 16826586 (1) V)
(vatten 50409046 (3) V)
(vergeten 16855388 (1) V)
(verleren 16855784 (1) V)
(verstaan 33633593 (2 4) V)
(remember)
(to ignore)
(to imply)
(to see the point)
(to know)
(to misunderstand)
(to misunderstand)
(to recognize)
(to deny)
(to understand)
(to forget)
(to forget)
(to understand)
Some of these are already related to WETEN (know) in Vlis: “inzien”, “kennen”, “vatten”. Most of the
new cases refer to dynamic changes which result in a state of knowing but cannot be seen as hyponyms
or hyperonyms of the state knowing. We find also some antonyms which will be further discussed
below.
If we apply the Acquilex-tools for finding synonyms (described above) we get the following
results:
Extracting equivalents using the bilingual dictionaries
Following startword has been selected:
(weten 84581 (1 2 3 4) V) (begrijpen 5812 (1) V) ((kennen 32733 (2) V))
Currently Processing item:
(weten 84581 (1 2 3 4) V)
Translation Circles:
(tell
(weten uitmaken zeggen kennen))
(realize (beseffen weten zich_realiseren zich_bewust_zijnworden))
(know (weten bewust_zijn zich_bewust_zijn beseffen kennis_hebben kennis_hebben_van))
Currently Processing item:
(begrijpen 5812 (1) V)
LE2-4003
EuroWordNet
87
EuroWordNet D006: Definition of the links and subsets for verbs
Translation Circles:
(grasp
(vatten omvatten begrijpen))
(comprehend (vatten be_vatten doorgronden doorzien begrijpen))
(take
(begrijpen in_zich_opnemen beseffen doorzien))
(take
(begrijpen beschouwen aannemen opvattennemen snappen))
(understand
(begrijpen verstand_hebben_van verstaan snappen inzien vatten be_vatten))
(understand
(begrijpen begrip_hebben_voor))
(understand
(begrijpen vernemen aannemen uit_opmakenafleiden er_uit_opmakenafleiden))
(understand
(begrijpen het_begrijpen het_snappen))
Search completed!!!!!!
Currently Processing item:
(kennen 32733 (2) V)
Translation Circles:
(know (kennen machtig_zijn bekendvertrouwd_zijn_met))
(know (kennen onderhevig_zijn_aan ervaren ondergaan))
Search completed!!!!!!
Potential Equivalents from the Translation Circles:
(vernemen 78759 (2) V)
(hear)
(verstaan 79161 (2) V)
(understand)
(aannemen 317 (4) V)
(accept as a view)
(beschouwen 6605 (1) V)
(consider)
(beseffen 6621 (1) V)
(realise)
(doorzien 15632 (1) V)
(understand)
(doorgronden 15427 (1) V)
(understand)
(snappen 67414 (2) V)
(understand)
(bevatten 6979 (2) V)
(understand)
(vatten 77398 (3) V)
(understand)
Most of the co-translations in the translation-circles are not entries or could not be found (due to the
reflexives or the nominalized form). Most acceptable words represent synonyms of “begrijpen” in Vlis.
Some of the new cases are not really synonyms but refer to events causing a state of knowing, e.g.
“beschouwen” (consider), “vernemen” (hear). Only “verstaan” (understand) is a new synonym not yet
included in Vlis.
If we look at the explicit synonym fields in the Van Dale monolingual Dutch dictionary we
again come across dynamic, event-denoting words which are wrongly presented as synonyms:
“bespeuren” (notice), “gewaarworden” (perceive), “erkennen” (admit). This strategy does not result in
new synonyms which have not been included in Vlis.
LE2-4003
EuroWordNet
88
EuroWordNet D006: Definition of the links and subsets for verbs
We also did not find cases of similar definitions (consisting of several words) for the concepts
directly related to WETEN (one-word definitions are not considered as good candidates). Some
overlapping definitions have been found for the following indirectly related verbs:
•
onderwijzen: 1
(educate)
bijbrengen: 1
(teach)
opleiden: 1
(train)
bekwamen: 1
(train)
kennis , begrip of vaardigheid overbrengen
(to carry over knowledge, understanding or skill)
<kennis> verschaffen , eigen laten maken
(to provide <knowledge>,…)
de nodige kennis en vaardigheid bijbrengen
(to provide the necessary knowledge and skills)
de nodige kennis en vaardigheid bijbrengen
(to provide the necessary knowledge and skills)
•
misvatten: 1
(misunderstand)
misverstaan: 1
(misunderstand)
verkeerd opvatten , begrijpen
(consider, understand wrongly)
verkeerd begrijpen
(understand wrongly)
From these definitions we can at least derive the following synsets:
{onderwijzen, bijbrengen, opleiden, bekwamen}
{misvatten, misverstaan}
which both are related to WETEN in an indirect way: causing it and denying it respectively.
Starting from WordNet 1.5 did not result in any new synonyms. The synset for knowing in
WN1.5. is {know cognize} which does not provide new translations.
Concluding so far we can say that, a poorly lexicalized domain as knowing, apparently does not
provide much material for extracting more hyponymy and synonymy relations. This may be due to the
fact that there simply are not many related words or that the resources do not provide sufficient
information in a consistent way to be able to extract these relations. However, as a non-dynamic, static
verb we can expect that there are many more verbs denoting a change resulting in a state of knowing.
Typically, we can think of the following types of events that denote such changes:
LE2-4003
EuroWordNet
89
EuroWordNet D006: Definition of the links and subsets for verbs
• communication verbs which result in the dissemination of information and therefore have a state of
knowing this information as a result.
• mental-action verbs leading to particular states of knowing
• perception verbs leading to states of knowledge
• educational actions leading to the acquisition of knowledge, such as reading, studying, learning,
teaching, etc..
When we look at causative definition patterns of know-related events these groups clearly emerge. In
the following examples this is evident for definitions in which “kennis” (knowledge) is the object of
some transaction, by teaching and/or communication:
inlichten: 1
(inform)
Relations:
de nodige kennis verschaffen , opheldering geven
(to provide the necessary knowledge, to give clarification)
inlichten: 1 CAUSES
kennis
onderwijzen: 1
(educate)
Relations:
kennis , begrip of vaardigheid overbrengen
(to carry over knowledge, understanding or skill)
onderwijzen: 1 CAUSES
kennis
leren: 2
(learn)
Relations:
bedrevenheid , kennis verwerven in
(to acquire skills and knowledge in)
leren: 2
CAUSES
kennis
When we look at the usual patterns for causation definitions we find the following cases:
_
doen (do, make) + V infinitive
uitleggen: 1
(explain)
Relations:
doen begrijpen
(make understand)
uitleggen: 1 CAUSES
uitleggen: 1
bekendmaken: 1
(announce)
Relations:
HAS_HYPERONYM
openbaar maken , doen weten
(to make public, make known)
bekendmaken: 1
CAUSES
bekendmaken: 1
LE2-4003
begrijpen
(understand)
doen (do, act)
weten
(know)
HAS_HYPERONYM doen (do, act)
EuroWordNet
90
EuroWordNet D006: Definition of the links and subsets for verbs
_
laten (let) + Vinfinitive
melden: 1
(announce)
Relations:
berichten , laten weten
notify, let know
melden: 1
CAUSES
melden: 1
waarschuwen: 2
(warn)
Relations:
HAS_HYPERONYM
verwittigen , laten weten
notify, let know
waarschuwen:2
CAUSES
waarschuwen:2
_
weten
(know)
doen (do, act)
weten
(know)
HAS_HYPERONYM doen (do, act)
om te (in order to) + Vinfinitive
verkennen: 1
(explore)
Relations:
uitgaan om kennis trachten te verwerven over
(to go out in order to try to gain knowledge about)
verkennen:1 CAUSES
verwerven
(acquire)
CAUSES
kennis
(knowledge)
verkennen:1 HAS_HYPERONYM
uitgaan (go out)
aanleren: 1
zich <een kennis of vaardigheid> eigen maken
(teach)
(acquire <a knowledge or skill>)
Relations:
aanleren:1
CAUSES
kennis
(knowledge)
aanleren:1
HAS_HYPERONYM
eigen maken
(acquire)
aanleren: 2
(teach)
Relations:
<een kennis of vaardigheid> onderwijzen
(teach <knowledge or skill>)
aanleren:2
CAUSES
aanleren:2
LE2-4003
HAS_HYPERONYM
kennis
(knowledge)
onderwijzen (teach)
EuroWordNet
91
EuroWordNet D006: Definition of the links and subsets for verbs
_
te (to) Vinfinitive komen (get)
uitvinden: 2
(find out)
Relations:
te weten komen
(to get to know)
uitvinden:2 CAUSES
weten (know)
achterhalen: 3
(find out)
Relations:
ontdekken , te weten komen
(discover, to get to know)
achterhalen:3 CAUSES
weten (know)
Typically, we see here that many causal patterns do not mention any of the know-verbs but refer to
“kennis” (knowledge) as the object of some action. Only indirectly we can infer from this that a state of
knowing is the result.
Looking for conjunction of events resulted in the following examples of SUBEVENT relations:
ontcijferen: 1
(decode)
Relations:
<iets onduidelijks> lezen en begrijpen
(<something unclear) to read and understand)
ontcijferen:1 SUBEVENT
lezen (read) c1
ontcijferen:1 SUBEVENT
begrijpen (understand) c2
bestuderen: 1
met aandacht lezen en overwegen om de betekenis ten volle te begrijpen
(study)
(to read and consider carefully in order to understand the full meaning)
Relations:
bestuderen:1 CAUSES
begrijpen
non-factive
(understand)
bestuderen:1 SUBEVENT
lezen (read) c1
bestuderen:1 SUBEVENT
overwegen (consider) c2
The first example is not fully straight forward. Although “ontcijferen” is represented here as a separate
embedded state it is really the result of “lezen” (read). In the case of “bestuderen” this is done in a
more correct way: “begrijpen” is the resulting state and the other verbs are subevents of embedding
situations possibly (non-factive) leading to it.
LE2-4003
EuroWordNet
92
EuroWordNet D006: Definition of the links and subsets for verbs
When we look for definitions with negative elements we find the following potential antonyms:
•
achterhouden: 2
(to keep back)
niet mededelen, geheimhouden
(to not report, to keep as a secret)
geheimhouden: 1
(keep as a secret)
<iets> niet openbaren , ...
(to not make<something> known, ...)
Relations:
•
{achterhouden, geheimhouden}
NEAR_ANTONYM
{mededelen, openbaren} (report, make known)
denken: 3
(remind)
niet vergeten
(not forget)
vergeten: 1
(forget)
uit het geheugen verliezen
(to loose from memory)
vergeten: 3
(forget)
Relations:
niet meer bewust zijn van , niet denken aan
(to be no longer conscious of, not think of)
denken: 3
NEAR_ANTONYM
vergeten:1, 3 (forget)
•
verleren: 1
(unlearn)
na enige tijd niet meer kennen of kunnen
(after a while no longer being able or know)
•
misvatten: 1
(misunderstand)
verkeerd opvatten , begrijpen
(consider, understand wrongly)
misverstaan:1
(misunderstand)
verkeerd begrijpen
(understand wrongly)
Relations:
{misvatten, misverstaan}
NEAR_ANTONYM begrijpen
(understand)
Some of these have been described above. They all refer to events in which either a state of knowing or
the negation of a state of knowing is the result. These events can be communication verbs or mental
acts resulting in having access to knowledge. Some of them form pairs, but “verleren” (unlearn) is in
an opposition with “kennen” (know) which is too general.
We can expect that there will not be many cross-part-of-speech relations with “weten” (know).
As a non-dynamic verb we can exclude concepts where “weten” is the cause of another event. The
reverse situation that another verb represents the cause of knowing something has already been
discussed above. What is left then are hyponymy and synonymy across part-of-speech. In this respect,
LE2-4003
EuroWordNet
93
EuroWordNet D006: Definition of the links and subsets for verbs
we already mentioned the nouns “weten” (the knowing) and “kennis” (knowledge). Scanning the
typical definition patterns for these relations only yielded the following results:
(begrijpelijk 16783027 (1) A)
(onkenbaar 16826260 (1) A)
(onbegrijpelijk 16825257 (1) A)
(onbegrip 16825258 (1) N)
(begrip 67114678 (4) N)
(comprehensive)
(unrecognizable)
(uncomprehensive)
(misunderstanding)
(understanding)
The first three cases are adjectives which refer to the capability to be understandable or not. This
cannot be expressed using the current set of relations. The last two nouns are antonyms where “begrip”
(understanding) is synonymous to “begrijpen” (to understand) and can be linked by the
XPOS_HAS_SYNONYM relation.
4.2.3 Conclusion
The general conclusion is that the Vlis relations can be taken as a starting point for deriving the
EuroWordNet database. Where possible this information will be improved by:
•
•
•
extracting additional information from definitions
using the bilingual dictionaries
comparing wordnets cross-linguistically
Special attention will be paid to relations which are unreliable or ambiguous, to phenomena
such as transitive/intransitive alternation of verbs, or verb compounds of which we know that they may
have lead to wrong decisions.
Furthermore, some relations (e.g. entailment, roles) are missing in the Vlis database and have to
be derived by systematically looking for patterns expressing it. In general we can apply the following
techniques for improving data and adding links:
•
•
•
•
•
•
extracting definition patterns and structure for relevant relations.
check for the badly motivated ends in Vlis relations.
check frequent combinations of hyperonyms for synonymy, disjunct or conjunct interpretations. If
disjuncts are too extreme the synsets have to be differentiated again. Since multiple hyperonyms
are not allowed in VLIS in our case we have to find these multiple cases by:
• looking for deviating and coordinated genus words in definitions
• looking at other senses
• checking the association and reference relations
• enlarging the synsets with bilinguals
look for phrases in definitions to find missing children
look for top-concepts to find potential combinations of hyperonyms
look at classifications in other sources to find alternative hyperonyms
LE2-4003
EuroWordNet
94
EuroWordNet D006: Definition of the links and subsets for verbs
•
•
generate synsets using bilinguals to find new synonyms
check the different meanings of the entries by looking for frequent pairs of hyperonyms that are
applied to different meanings if the same word.
The extraction of information from the definitions was done in an experimental way, but have
shown that many regular patterns can be found that provide additional information. This information is
certainly useful provided of course that it is clear what we are looking for and what the implications
are.
We have focused the discussion on the more general levels of knowing and moving. At this
level we expect that the relations are least clear and most problems are concentrated. At more specific
levels the given relations are more reliable and information is more explicit and therefore more easily
extractable.
LE2-4003
EuroWordNet
95
EuroWordNet D006: Definition of the links and subsets for verbs
4.3 First Results of work on Italian
4.3.1 Motion Verbs
In order to try the encoding of relations for Italian motion verbs, we have first examined our
Italian LDB aiming at extracting all the hyponyms (and their synsets) of the verb muoversi (= to
move, indicating motion by the protagonist of the event, and roughly corresponding to the synset
selected in WN 1.5 for the experiment). Before trying either to build synsets out of the data on
synonymy or to build taxonomies, however, it was necessary to perform a sense disambiguation of all
the synonym/hypernym verb homographs: actually, as already explained above (§ 3.1.1), within the
LDB only relations among verb entries and verb homographs are identified, while the particular sense
in which a verb is synonym/hyperonym of another is not coded. Moreover, some information had to be
added or modified manually, in order to correct errors and inconsistencies found within the LDB, and
some changes were made to the original source. A major problem of carrying out a work of this kind,
however, is that of the subjectivity of the decisions taken: in fact, we must remind that, as expected in
the area of semantics, our intuitions not only are sometimes different from data found in dictionaries
but are also sometimes different from those of other speakers. In the following, we shall i) provide a
general review of the problems encountered/solutions (preliminarily) identified; ii) provide examples
of relations encoded. In the Appendix, the muoversi taxonomy is then reported for reference.
4.3.1.1 Major problems encountered / solutions identified
1)
A problem we encountered several times was that of a not coherent distinction of senses for
verbs referring either to motion along an unbounded path or to motion along a bounded path. This is a
relevant distinction in Italian, given that not all Italian motion verbs may refer to motion to a goal/from
a source (which is instead possible for all English motion verbs) (cf. Acquilex papers on this issue). On
the other hand, as we have already noted above, data on cross linguistic differences in lexicalisation
patterns are necessary for LE applications.
Different dictionaries of Italian, which we examined, distinguish different senses for the same
verb and also within the same dictionary there are verbs for which different senses (unbounded and
bounded) are distinguished and others for which a similar distinction is not formulated, although they
however display a similar syntactic behaviour/semantic reference. Therefore, we merged information
coming from different sources and added other information manually when necessary. This was done,
for instance, with respect to andare (to go) (but also in relation to other verbs: correre (to run), volare
(to fly), etc.). In fact, this verb can be connected with two fundamental (motion)18 senses: one referring
to undirected motion (or motion along an unbounded path) and the other referring to directed motion
18
Andare also has ‘non-motion’ senses similarly to the English to go. For the moment, however, only the fundamental
motion senses have been taken into consideration.
LE2-4003
EuroWordNet
96
EuroWordNet D006: Definition of the links and subsets for verbs
(motion along a bounded path, i. e., motion to a goal or from a source: in other words, change of
position). It is also indicated in dictionaries as a synonym of a number of verbs:
- gire
- ire
- muovere (intr.) 0_1 (to move)
- recarsi 0_2b (to go to a place)
- portarsi 0_1 (to go to a place) (synonym of trasferirsi 0_0 = to change position)
The first two verbs are archaic variants of andare itself and thus we have not considered them;
(intransitive) muovere (to move) refers to undirected motion, whereas recarsi (to go to) and portarsi
refer to motion to a goal (change of position). Two fundamental synsets have thus been created for
andare:
Sense 1
andare, muovere -- (to go along an unbounded path)
Sense 2
andare, recarsi, portarsi, trasferirsi -- (to go to a place, to change position).
The two synsets both appear to match with Sense 1 of to go in WordNet 1.5, since the
difference between undirected/directed motion is not relevant in English, given that, as recalled above,
all English motion verbs may refer to motion to a goal/from a source (have a derived ‘directed
motion’ sense). In Italian, instead, the difference is relevant since only a (relatively small) subset of
motion verbs may refer to motion to a goal (or from a source) and this has consequences on the
syntactic properties of the different subsets of Italian motion verbs (cf. the difference between correre
and nuotare: the former may either refer to motion along an unbounded path - Corse per ore (He/She
ran for hours) - or along a bounded path - Corse a casa (He/She ran home) -; the latter only refers to
motion along an unbounded path - Nuotò per 10 minuti / * a riva (He/She swam for 10 minutes / (*)
to the bank)).
2)
If andare and similar verbs have two senses that map to just one in WN 1.5, other verbs do not
have the ‘directed motion’ sense: e. g., nuotare (to swim), camminare (to walk), etc. (which, in fact,
cannot occur with directional phrases). These verbs were preliminarily connected with their ‘partially
correspondent’ verbs in English (to swim and to walk), however we think that the link ‘Involved’ is
necessary to clarify the semantic characteristics of them, i. e., the different lexicalisation patterns of
semantic components within their roots with respect to their English counterparts.
3)
In general Italian synsets have been built both using explicit data on synonymy within the LDB
(words indicated as synonymous of an entry) and ‘implicit’ data (single words used to define an entry:
e. g. correre 0_3 (to run) is defined as ‘accorrere’ (to run to a place) and we have considered them as
synonyms). However, sometimes both our intuitions and the data from the bilingual (Italian-English)
have led us to build synsets using verbs which were not related in the Italian dictionaries. For instance,
girandolare, girellare, gironzolare and girovagare have very similar semantic references and, in fact,
LE2-4003
EuroWordNet
97
EuroWordNet D006: Definition of the links and subsets for verbs
can be all translated as “to saunter”, “to stroll”, thus we have decided to put them in a same synset,
even if they were not indicated as synonyms within dictionaries.
4)
Sometimes, within a dictionary, a verb is defined as a hyponym of another verb even if another
dictionary or the speaker’s intuitions suggest that the two verbs are synonymous. This is the case of
procedere (to go on) defined as “andare avanti, proseguire il cammino”, that is as hyponym of both
andare and proseguire, whereas proseguire itself is defined as “andare avanti” and seems actually to be
a synonym of procedere (and can, indeed, be translated also as ‘to go on’). In this and similar cases,
synsets were built containing the verbs encountered.
5)
Further problems were then encountered in trying to match our verb senses to WN 1.5. Some
verbs, indeed, have a correspondent in WN 1.5 only for one (or some) of theis senses. For example,
allontanarsi (to go away; to go far) can be put in two synsets:
Sense 1
allontanarsi, discostarsi, muoversi, scostarsi -- (to go away)
(Luigi si allontanò da quella casa - Luigi went away from that house)
Sense 2
allontanarsi, andare lontano -- (to go far)
(Non allontanarti troppo - don’t go too far)
However there is a problem of correspondence with WN 1.5, since different synsets are found
there corresponding to the first sense, but there is no synset exactly corresponding to the second:
3 senses of go away
Sense 1
go, go away, depart, travel away -- (travel away from a place into another direction; "Go away before I start to cry"; "When
did he go to work today?" "The train departs at noon")
Sense 2
leave, go forth, go away
=> go, go away, depart, travel away -- (travel away from a place into another direction; "Go away before I start to
cry"; "When did he go to work today?" "The train departs at noon")
Sense 3
disappear, vanish, go away -- (get lost; "He disappeared without a trace")
2 senses of go far
Sense 1
go far, go deep -- ("His accomplishments go far")
=> run, go, pass, lead, extend -- (run or extend between two points; "Service runs all the way to Cranbury"; "His
knowledge doesn't go very far")
LE2-4003
EuroWordNet
98
EuroWordNet D006: Definition of the links and subsets for verbs
Sense 2
arrive, make it, get in, go far
=> succeed, come through -- (attain success)
In still another case, some Italian verbs have no English correspondent and so it is not possible
to indicate a direct connection with WN 1.5 (this happens, e. g., for coricarsi, which means “to go to
bed”).
6)
A problem we have not yet really dealt with is that of multi-words, even if in some cases they
have been put within synsets.
4.3.1.2 Searching for the relevant relations
The experiment carried out, besides being aimed at verifying problems in building the
individual wordnets, had the goal of explicitly defining the relations needed. Thus, in the following
points we shall summarize the results obtained with respect to each relation.
1)
Those of synonymy/hyperonymy/hyponymy are, of course, the fundamental relations on which
the structure of the database will be based. Indeed, they allow to make fundamental inferences: i) a
property of a verb is also a property of all its synonyms (apart from the case of antonymy holding
between variants, cf. above); ii) a property of a verb is inherited by all its hyponyms, even if in this
case it is always somehow restricted, or in some cases overridden, by more specific characteristics
displayed by hyponyms. This can be seen by the taxonomy provided in the Appendix: i.e., all the
hyponyms of the sense 2 of andare (indicated for the time being as 0_1b), i.e., of the {andare, recarsi,
portarsi, trasferirsi} synset have a basic reference to directed motion. The major problem which we
shall have to deal with in order to extract and then encode data on these relations will be that of the
sense disambiguation which needs to be carried out before extracting relations. However, since words
found at low levels in hierarchies have more specific meanings, most manual work will be done to
disambiguate top words, while then semi-automatic procedures can be used.
2)
The INVOLVED relation seems also very important (as we have stated in section 2) in order to
code more detailed semantic information on verbs. Unfortunately, for the time being, and within the
scope of the project, the development of the complex procedures needed in order to extract data on it is
not generally feasible (cf. § 2.5), however some data already (manually) extracted can be used in our
case. For instance, within the Acquilex project an analysis of different taxonomies was carried out for
encoding data of this kind; thus the results obtained can be used, by making the necessary changes
connected with the characteristics of the relation identified in EuroWordNet. So, for instance, if we
take again into consideration some verbs already reported in section 2, we can encode for them the
relations indicated below:
camminare = “andare a piedi”
to walk
to go on foot
LE2-4003
EuroWordNet
99
EuroWordNet D006: Definition of the links and subsets for verbs
INVOLVED_INSTRUMENT --> piedi
cavalcare =
“andare a cavallo”
to ride
to go on a horse
INVOLVED_INSTRUMENT --> cavallo
coricarsi =
“andare a letto”
to go to bed
INVOLVED_TARGET_DIRECTION --> letto
uscire =
“andare, venire fuori”
to go out
to go, to come out
INVOLVED_TARGET_DIRECTION --> fuori.
3)
As far as the other relations are concerned, we have already remarked which strategies can be
used to extract data on them (if they are indicated). However, by this first experiment on data some
additional hypotheses in this respect have been formulated. For instance, we have found definitions in
which a pattern is used to indicate the goal of an action referred to by a verb (per V= in order to V);
when a pattern of this kind is present in a definition, a SUBEVENT relation may be identified. This
seems the case of passare (to pass, defined as “muoversi attraversando un luogo per andare in un altro”
- to move crossing a place to go to another), which could be seen as a subevent of andare (to go).19
Another pattern which can be used to detect a relation between words is per effetto di V/N (as an effect
of), which indicates the ‘cause’ of a situation. For instance, beccheggiare (to pitch) is defined as
“oscillare di una nave per effetto del moto ondoso” (to swing (said) of a ship as an effect of waves
motion): therefore we may state a causal relation between the waves motion and the event. Also in
these cases, however, data will be encoded only for those verbs for which either we have already
extracted information or we may easily extract it.
19
And in fact also the pair buy/pay, which has been used above to give an example of the relation, could be connected by
such construction: one has ‘to pay in order to buy’.
LE2-4003
EuroWordNet
100
EuroWordNet D006: Definition of the links and subsets for verbs
4.2.2 Know verbs
As far as ‘know verbs’ are concerned, in general, problems similar to those already reported
with respect to motion verbs were encountered. Furthermore, we do not find much useful information
within dictionaries, at least if we only consider synonymy and hyponymy relations. Sapere (to know)
and its synonym conoscere, for instance, have generally essere (to be) and avere (to have) as genuses:
these only transfer to their hyponyms the indication of a semantic reference to a state, whereas much
important semantic information is conveyed by adjectives or phrases following them within definitions.
Thus, in order to be able to indicate all the meaning facets of the two verbs, we need links other than
simply synonymy or hyponymy. Furthermore, sapere and conoscere refer, in their fundamental sense,
to a state, however what’s interesting of these verbs is their ability to refer to a change-of-state when
inflected in certain tempo-aspectual forms:
a. Giovanni sapeva/conosceva la verità.
Giovanni knew the truth (meaning that he was conscious of the truth)
b. Improvvisamente Giovanni seppe/conobbe la verità.
Suddenly Giovanni knew (discovered) the truth.
An indication of such a property is explicitally given in examples found within dictionary
definition, but we think that it could be better given by connecting the two verbs to a genus referring to
the inception of the awareness. Therefore, in order to extract synonymy and hyponymy relations we
tried to intervene quite ‘heavily’ on the data found in dictionaries (either connecting sometimes
taxonomies wich are not really connected within the dictionary or changing the status of the relations
found: i.e. from hyponymy to synonymy and viceversa). As far as the other relations are concerned, we
encountered problems of the kind already discussed for motion verbs. (Cf. the Appendix for the synsets
identified and for the taxonomies built.)
4.4 First results of work on Spanish
The Spanish approach is mainly based on extraction of information by means of automatic
methods. We have two main sources: an MRD: VOX, and WN1.5. As in the case of nouns (see D005
for details), Spanish words will be automatically mapped to WN 1.5 synsets, thus finally obtaining a
Spanish Verb lattice somewhat parallell to that of WN 1.5. As an effect, we hypothesize that
verb-to-verb relations applying in WN1.5 will be adequate for its Spanish counterpart. Nevertheless a
process of manual validation will be obviously needed. This way, WN1.5 will be taken as the main
source of information while that extracted from MRD-VOX will be used to refine, improve and modify
this basic source.
LE2-4003
EuroWordNet
101
EuroWordNet D006: Definition of the links and subsets for verbs
4.4.1 Subset selection of motion verbs and ‘know verbs’
In the Spanish experiment MRDs and derived taxonomies have been used as source for testing
potential both knowledge and movement subsets. In principle, verbs of moving should be those being
hyponyms of either 'ir', 'mover' and 'moverse'. On the other hand, ‘saber' and 'conocer' have proved to
be the best sources for deriving taxonomies to be used as verbs of knowing.
These subsets obtained in this first step pose some problems concerning their semantic
boundaries specially in the move taxonomy. Often, actions involve a movement of some kind but are
not properly "movement actions". In these cases is very difficult to decide even manually if a sense
belongs or not to a taxonomy. For movement verbs we have defined a basic criterium in order to help
the decision task:
A sense belongs to movement taxonomy iff
the main purpose of the action is movement
This criterium helps us to reject senses like: 'hojear', (to turn the
etc.). This criterium has to be applied manually.
pages of), 'fregar' (to rub, to wash,
4.4.2. Extraction of relations
Hyponym
The source for the extraction of hyponym and hyperonym relations is derived from taxonomies
from MRD-VOX. We assume that this source is sufficient enough for obtaining these relations. The
problem mentioned in the selection of the subset has an effect on the extraction of hyponym relations: a
manual process of senses removal is needed and this process will reduce the number of hyponym
relations given in the dictionary.
Synonym
MRD-VOX has a labeled field for this relation, and it can be extracted automatically, but
frequently this information does not appear explicitly. For establishing exhaustively this relation others
methods had to be used. The best solution should be to have a synonym dictionary available in MRD
format, but currently we don't have access to this kind of sources. Another source for extracting
synonymy will derive of our approach of automatically mapping Spanish words to WN1.5 synsets.
Obviously, words which map onto the same WN1.5 synset ought to be considered synonyms but also
in this case all the relations extracted would had to be checked and validated.
Antonymy
LE2-4003
EuroWordNet
102
EuroWordNet D006: Definition of the links and subsets for verbs
Like synonymy, antonymy can be extracted automatically from MRD. There is a specific field
in MRD for this relation, but on the contrary of nouns, in verb entries there are very few antonyms. For
example, in the selected subsets there are only 3 antonym relations encoded in MRD. This fact forces
us to raise again how to extract this relation.
A possible solution could be to apply a morphological analysis. The derivational Spanish
process allows to deduce antonym relations from pairs like 'conocer' / 'desconocer' (to know / not to
know), where the preffix 'des-' (and also 'a-', 'in-', etc.) means the opposite of 'conocer'.
Has_Subevent & Cause
Has_Subevent and Cause are the most difficult relations for automatic extraction. For Spanish
there is not information about them in the sources available. It seems that for detecting these relations
some world knwoledge is needed, therefore the main way for establishing them has to be manually. As
pointed out above it must be noticed that Has_Subevent and Cause relations which apply between two
English synsets are expected to keep on working between the Spanish synsets which are their
synonyms. Therefore, the main approach for building the Spanish WordNet, namely mapping from
Spanish to WN 1.5 synsets, will itself be a source of detection of Entailment and Cause relations.
Notwithstanding, relations thus stated will need to undergo a process of manual validation
Involvement relations
The Involvement_agent can be extracted from several definitions patterns. The patterns we find
for agent are the following:
'Persona que' +V
'Individuo que' +V
'Hombre que' +V
'Mujer que' +V
(person that)
(person that)
(man that)
(woman that)
In these patterns the nouns (Persona, individuo,etc.) are the genus term. In this cases, the entry is the
agent of the action referred by the verb.
The Involvement_instrument relation can be extracted from intrument taxonomy. The senses of
this taxonomy are in role instrument relationship with the verb that appears in their definitions after the
pattern:
LE2-4003
EuroWordNet
103
EuroWordNet D006: Definition of the links and subsets for verbs
'que sirve para' +V (used for)
'que se usa para' +V (")
'para' +V
(")
'usado para' +V
(")
This verb has always a finite form.
Summary of motion verbs relations:
tops
senses hyponyms
IR
MOVER
MOVERSE
synonyms
227
226
163
154
TOTAL
antonyms
37
162
153
544
20
16
541
0
0
0
73
0
Summary of know verbs relations
tops
senses
saber
conocer
18
TOTAL
LE2-4003
hyponyms
3
21
synonyms
antonyms
2
0
0
17
2
2
19
2
2
EuroWordNet
104
EuroWordNet D006: Definition of the links and subsets for verbs
5. Appendix
5.1 List of tests for identifying the relations in each language
5.1.2 Tests for Dutch
Dutch test 1
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Dutch test 2
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Synonymy between verbs
Test sentence
als iets/iemand/het Xt dan Yt iets/iemand/het
(if something/someone/it Xs the something/someone/it Ys)
als iets/iemand/het Yt dan Xt iets/iemand/het
(if something/someone/it Ys the something/someone/it Xs)
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
als iets/iemand/het begint dan start iets/iemand/het
als iets/iemand/het start dan begint iets/iemand/het
X = begint (begins)
Y = start (starts)
synset variants {beginnen V, starten V}
Synonymy of nouns and verbs denoting events and states
Test sentence
Als er sprake is van een X dan Yt er iets/iemand/het
(if there is a case of a/an X then something/someone/it Ys)
als iets/iemand/het Yt dan is er sprake van een X
(if something/someone/it Ys then there is a case of a/an X)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Als er sprake is van een beweging dan beweegt er iets
als iets beweegt dan is er sprake van een beweging
X = beweging (movement)
Y = beweegt (move)
beweging N XPOS_NEAR_SYNONYM bewegen V
bewegen V XPOS_NEAR_SYNONYM beweging N
EuroWordNet
105
EuroWordNet D006: Definition of the links and subsets for verbs
The distinction between hyponymy and synonymy is not always clear-cut. Sometimes concepts can be
very close showing only a very limited specialization. In the case of relations across part-of-speech we
can at least formulate the extra conditions that a strong morphological link between the two words is
preferred, which is the case of “beweging” (movement) and “bewegen” (move).
A specific test elicits a stronger intuition than a more general test. In general we therefore try to
formulate more specific tests in addition to the more general test. As a rule of thumb first the specific
test should be applied. If this yields a positive score than the relation can be assigned. If it yields a
negative score we can apply the general test. Whereas the above test works both for non-dynamic states
and dynamic events, the next test only applies to dynamic events:
Dutch test 3
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Synonymy between event denoting nouns and verbs
Test sentence
als er een X plaatsvindt dan Yt er iets/iemand/het
(if an X takes place then something/somebody/it Ys)
als iets/iemand/het Yt dan vindt er een X plaats
(if something/somebody/it Ys then a/an X takes place)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
als er een beweging plaatsvindt dan beweegt er iets/iemand
als iets/iemand beweegt dan vindt er een beweging plaats
X = beweging (movement)
Y = beweegt (move)
beweging N XPOS_NEAR_SYNONYM bewegen V
bewegen V XPOS_NEAR_SYNONYM beweging N
EuroWordNet
106
EuroWordNet D006: Definition of the links and subsets for verbs
The next test only applies to non-dynamic states expressed by nouns and verbs:
Dutch test 4
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
Synonymy between state-denoting nouns and verbs
Test sentence
Als er sprake is van een toestand van X dan Yt iets/iemand/het
(if there is a state of X then something/someone/it Ys)
als iets/iemand/het Yt dan is er sprake van een toestand van X
(if something/something/it Ys then there is a state of a/an X applies)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Als er sprake is van een toestand van slaap dan slaapt er iets
als iets slaapt dan is er sprake van een toestand van slaap
X = slaap (sleep)
Y = slaapt (sleeps)
slaap N
XPOS_NEAR_SYNONYM slapen V
slapen V
XPOS_NEAR_SYNONYM slaap N
Als er sprake is van een toestand van gelijkenis dan lijkt er iets
als iets lijkt dan is er sprake van een toestand van gelijkenis
X = gelijkenis (equality, similarity)
Y = lijkt (equals, is similar)
gelijkenis N XPOS_NEAR_SYNONYM lijken V
lijken V
XPOS_NEAR_SYNONYM gelijkenis N
The next test elicits synonymy between adjectives and verbs that denote non-dynamic states:
LE2-4003
EuroWordNet
107
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 5
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Synonymy between state-denoting verbs and adjectives
Test sentence
Als iets/iemand/het Xt dan is iets/iemand/het Y
(if something/someone/it Xs then something/someone/it is Y)
als iets/iemand/het Y is dan Xt iets/iemand/het
(if something/someone/it is Y then something/someone/it Xs)
- X is a verb in the third person singular
- Y is an adjective
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Als iets/iemand/het vreest dan is iets/iemand/het bang
als iets/iemand/het bang is dan vreest iets/iemand/het
X = bang (afraid)
Y = vreest (fear)
vrezen V
XPOS_NEAR_SYNONYM bang A
bang Aq
XPOS_NEAR_SYNONYM vrezen V
For hyponymy relations between verbs a separate test is used which makes the notion of troponym (a
more specific way of) explicit as defined by Fellbaum (1991):
Dutch test 6
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Hyponymy-relation between verbs as troponyms
Test sentence
als iets/iemand/het Xt dan Yt iets/iemand/het op een bepaalde manier
(if something/someone/it Xs the something/someone/it Ys in a particular way)
als iets/iemand/het Yt dan Xt iets/iemand/het op een bepaalde manier
(if something/someone/it Ys the something/someone/it Xs in a particular way)
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- there is a manner-PP that has to be added to the Y-phrase to match it with the
X-phrase
als iets/iemand/het zwemt dan beweegt iets/iemand/het op een bepaalde manier
als iets/iemand/het beweegt dan zwemt iets/iemand/het op een bepaalde manier
X = zwemt (swims)
Y = beweegt (moves)
zwemmen V HAS_HYPERONYM
bewegen V
bewegen V HAS_HYPONYM
zwemmen V
In addition to specific manners of events verbs can also refer to more specific events covering any
other features. These features may be expressed by any additional PP or specification. A verb such as
“vertrekken” (to leave) should rather be defined as “to move away from something” than by “to move
LE2-4003
EuroWordNet
108
EuroWordNet D006: Definition of the links and subsets for verbs
in a certain way”. Following this the Dutch test sentence 6 can also be re-formulated in a more general
way where the additional PP can express any aspect and not just manner:
Dutch test 6 (revised)
Comment:
Hyponymy-relation between verbs reflecting any specification
Score
Test sentence
yes
a
als iets/iemand/het Xt dan Yt NP (NP) PP
(if something/someone/it Xs the something/someone/it Ys PP)
no
b
als iets/iemand/het Yt dan Xt iets/iemand/het PP
(if something/someone/it Ys the something/someone/it Xs PP)
Conditions:
- X is a verb in the third person singular form
- Y is a verb in the third person singular form
- there are is at least one specifying NP or PP that has to be added to the
Y-phrase to match it with the X-phrase
Example: a
als iets/iemand/het vertrekt dan beweegt iets/iemand/het van iets vandaan
(if something/somebody/it leaves then it moves from something away)
b
als iets/iemand/het beweegt dan vertrekt iets/iemand/het van iets vandaan
(if something/someone/it moves then it leaves from something away)
X = vertrekt (leaves )
Y = beweegt (moves)
Effect:
vertrekken V HAS_HYPERONYM
bewegen V
bewegen V HAS_HYPONYM
vertrekken V
LE2-4003
EuroWordNet
109
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 7
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
Hyponymy of nouns and verbs denoting events and states
Test sentence
als er sprake is van een X dan Yt (er) iets/iemand/het + NP, PP (op een bepaalde
wijze)
(if there is a case of a/an X then something/someone/it Ys + NP, PP (in a certain
way))
als iets/iemand/het Yt dan is er sprake van een bepaalde X
(if something/someone/it Ys then there is a case of a/an certain X)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there should be at least one specifying NP or PP that makes the Y-phrase
equivalent to the X-phrase or the other way around.
- preferably there is no morphological link between the noun and the verb
als er sprake is van een moord dan doodt iets/iemand/het op ‘illegale’ wijze
?als iets/iemand/het doodt dan is er sprake van een moord
X = moord (murder)
Y = doodt(kills)
moord N
HAS_XPOS_HYPERONYM
doden V
doden V
HAS_XPOS_HYPONYM
moord N
?als er sprake is van een sport dan voetbalt er iets op een bepaalde wijze
als iets/iemand/het voetbalt dan is er sprake van een bepaalde sport
X = sport (sport)
Y = voetbalt (plays football)
sport N
HAS_XPOS_HYPONYM
voetballen V
voetballen V
HAS_XPOS_HYPERONYM
sport N
Here we see that the reverse of the score leads to a reversion of the hyponymy as well:
noun-to-hyperonym-verb or verb-to-hyperonym-noun. As long as one direction has a clear positive
score and the other direction has a clear negative score we are dealing with a hyponymy relation.
The standard “zijn” (be) construction for hyponymy can be extended with a manner-specification to
elicit troponymy relations between nouns and verbs:
LE2-4003
EuroWordNet
110
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 8
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
Manner-hyponymy or troponymy between nouns and verbs
Test sentence
(een/het/de) X is een wijze/manier/vorm van Y
((a/an/the) X is a way/manner of Y)
Y is een bepaalde X
(Y is a certain X)
- X is a noun in the singular
- Y is a verb in the infinitive form
- preferably there is no morphological link between the noun and the verb
voetbal is een manier van sporten
?sporten is een bepaald voetbal
X = voetbal (football)
Y = sporten (sport)
voetbal N
HAS_XPOS_HYPERONYM
sporten V
sporten V
HAS_XPOS_HYPONYM
voetbal N
?een sport is een manier van voetballen
voetballen is een bepaalde sport
X = sport (sport)
Y = voetballen (play football)
sport NHAS_XPOS_HYPONYM
voetballen V
voetballen V HAS_XPOS_HYPERONYM
sport N
Whereas the previous tests works both for non-dynamic states and dynamic events, the next test only
applies to dynamic events:
LE2-4003
EuroWordNet
111
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 9
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Hyponymy between event denoting nouns and verbs
Test sentence
als er (een/het/de) X plaatsvindt dan Yt (er) iets/iemand/het + NP, PP (op een
bepaalde wijze)
(if (a/an/the) X takes place then something/somebody/it Ys + NP, PP (in a certain
way))
als iets/iemand/het Yt dan vindt er (een/het) bepaald(e) X plaats
(if something/somebody/it Ys then (a/an/the) certain X takes place)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there should be at least one specifying NP or PP that makes the Y-phrase
equivalent to the X-phrase or the other way around.
- preferably there is no morphological link between the noun and the verb
als er gemompel plaatsvindt dan spreekt er iets/iemand op een bepaalde manier
?als iets/iemand spreekt dan vindt er bepaald gemompel plaats
X = gemompel (murmuring)
Y = spreekt (speaks)
gemompel N HAS_XPOS_HYPERONYM
spreken V
sprekenV
HAS_XPOS_HYPONYM
gemompel N
The next test only applies to non-dynamic states expressed by nouns and verbs:
LE2-4003
EuroWordNet
112
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 10
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Hyponymy between state-denoting nouns and verbs
Test sentence
als er sprake is van een toestand van X dan Yt (er) iets/iemand/het + NP, PP (op
een bepaalde wijze)
(if there is a state of X then something/someone Ys + NP, PP (in a certain way))
als iets/iemand/het Yt dan is er sprake van een toestand van bepaalde X
(if something/something Ys then there is a state of a/an certain X applies)
- X is a noun in the singular
- Y is a verb in the third person singular form
- there should be at least one specifying NP or PP that makes the Y-phrase
equivalent to the X-phrase or the other way around.
- preferably there is no morphological link between the noun and the verb
als er sprake is van een toestand van clubliefde dan houdt iermand van een club
(if there is a state of club-loving then somebody loves a club)
?als iemand van een club houdt dan is er sprake van een toestand van bepaalde
clubliefde
(?if someone loves a club then there is a state of certain club-loving)
X = clubliefde (love for a club N)
Y = houden van (love V)
clubliefde N HAS_XPOS_HYPERONYM
houden van V
houden van V HAS_XPOS_HYPONYM
clubliefde N
The next test elicits hyponymy between adjectives and verbs that denote non-dynamic states:
LE2-4003
EuroWordNet
113
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 11
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Hyponymy between state-denoting verbs and adjectives
Test sentence
Als iets/iemand/het Xt dan is iets/iemand/het Y
(if something/someone/it Xs then something/someone/it is Y)
als iets/iemand/het Y is dan Xt iets/iemand/het + NP, PP (op een bepaalde wijze)
(if something/someone/it is Y then something/someone/it Xs + NP, PP (in a
certain way))
- X is a verb in the third person singular
- Y is an adjective
-- there is at least one specifying adverb, NP or PP that applies to the X-phrase or
the Y-phrase
- preferably there is no morphological link between the noun and the verb
Als iets/iemand/het spaart dan is iets/iemand/het zuining met geld (sparing with
respect to money)
?als iets/iemand/het zuining is dan spaart iets/iemand/het op een bepaalde wijze
X = spaart (save up)
Y = zuinig (sparing)
sparen V
HAS_XPOS_HYPONYM
zuinig A
zuinig A
HAS_XPOS_HYPERONYM
sparen V
The next test elicits antonymy with an additional clause to verify that the word-pairs are within a
reasonable denotational range:
LE2-4003
EuroWordNet
114
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 12
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Antonymy of verbs and nouns
Test sentence
(een/de/het) X en Y zijn beide een soort/vorm van Z maar (een) X is het
tegenovergestelde van (een) Y
(X and Y are both a kind of Z but X is the opposite of Y)
the converse of a)
- X and Y are singular or plural nouns, or verbs in the infinitive form
- Z is a hyperonym of both X and Z
beginnen en stoppen zijn beide een vorm van doen maar beginnen is het
tegenovergestelde van stoppen
(to begin and to stop are both a way of acting but to begin is the opposite of to
stop)
stoppen en beginnen zijn beide een vorm van doen maar stoppen is het
tegenovergestelde van beginnen
(to stop and to begin are both a way of acting but to stop is the opposite of to
begin )
X = begint(begins)
Y = stopt (stop)
Z = doen (act)
beginnen V
ANTONYM stoppen V
stoppen V
ANTONYM beginnen V
No separate tests are needed for NEAR_ANTONYM (which holds between synsets).
XPOS_NEAR_ANTONYM can be tested by compensating for the difference in part-of-speech. The
above test can at least be used for cross-part-of-speech relations between nouns and verbs.
LE2-4003
EuroWordNet
115
EuroWordNet D006: Definition of the links and subsets for verbs
The next test elicits a factive causation relation (Lyons 1977), where the reversal of the relation is
automatically generated (not necessarily implied, hence the label reversed):
Dutch test 13
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
LE2-4003
Factive causation relation between verbs
Test sentence
X veroorzaakt Y
(X causes Y)
X heeft Y tot gevolg
(X has Y as a consequence)
X leidt tot Y
(X leads to Y)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
doden veroorzaakt sterven
doden heeft sterven tot gevolg
doden leidt tot sterven
?sterven veroorzaakt doden
?sterven heeft doden tot gevolg
?sterven leidt tot doden
X = doden (kill)
Y = sterven (die)
doden V
CAUSES
sterven V
sterven V
IS_CAUSED_BY
doden V
reversed
EuroWordNet
116
EuroWordNet D006: Definition of the links and subsets for verbs
Non-factive causation is elicited by tests that express a more complex modal relation. In that case both
relations receive the label non-factive. Note that one of the relations can still be the result of reversing
the relation, hence it also has the label reversed:
Dutch test 14
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
LE2-4003
Non-factive causation relation between verbs using a modal auxiliary
Test sentence
X kan Y veroorzaken
(X may cause Y)
X kan Y tot gevolg hebben
(X may have Y as a consequence)
X kan leiden tot Y
(X may lead to Y)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
zoeken kan vinden veroorzaken
zoeken kan vinden tot gevolg hebben
zoeken kan leiden tot vinden
? vinden kan zoeken veroorzaken
? vinden kan zoeken tot gevolg hebben
? vinden kan zoeken tot vinden
X = zoeken (search)
Y = vinden (find)
zoeken V
CAUSES
vinden V
non-factive
vinden V
IS_CAUSED_BY
zoeken V
reversed,
non-factive
EuroWordNet
117
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 15
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
LE2-4003
Non-factive causation relation between verbs using a modal constructions
Test sentence
X is doen/laten Y
(X is do/let Y)
X is Y doen/laten plaatsvinden
(X is Y do/let take place)
X is Y mogelijk maken
(X is to make Y possible)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
wijsmaken is doen/laten geloven
wijsmaken is geloven doen/laten plaatsvinden
wijsmaken is geloven mogelijk maken
?geloven is doen/laten wijsmaken
?geloven is wijsmaken doen/laten plaatsvinden
?geloven is wijsmaken mogelijk maken
X = wijsmaken (fool, make believe)
Y = geloven (believe)
wijsmaken V CAUSES
geloven V
non-factive
geloven V
IS_CAUSED_BY
wijsmaken V non-factive,
reversed
tonen is doen/laten zien
tonen is zien doen/laten plaatsvinden
tonen is zien mogelijk maken
?zien is doen/laten tonen
?zien is tonen doen/laten plaatsvinden
?zien is tonen mogelijk maken
X = tonen (show)
Y = zien (see)
tonen V
CAUSES
zien V
non-factive
zien V
IS_CAUSED_BY
tonen V
non-factive,reversed
EuroWordNet
118
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 16
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Non-factive causation relation between verbs using try
Test sentence
X is proberen/trachten te Y
(X is to try Y)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
zoeken is proberen/trachten te vinden
?vinden is proberen/trachten te zoeken
X = zoeken (search)
Y = vinden (find)
zoeken V
CAUSES
vinden V
non-factive
vinden V
IS_CAUSED_BY
zoeken V
non-factive,
reversed
As explained above, causal relations can hold between a dynamic cause and a result which can either
be dynamic or static. Information on the dynamicity of the result is however not expressed in terms of
different cause relations but can be inferred from the hyponymy-relations that hold for the result. In this
respect we can thus have nouns, verbs and adjectives as results of CAUSES, where the latter can only
denote non-dynamic results and nouns and verbs can both denote dynamic or non-dynamic results. The
tests are therefore not differentiated for the dynamicity of the result. On the other hand, they do have to
be differentiated for the part-of-speech to get well-formed test-sentences. The next test can be used to
elicit a causal relation between a verb or noun on the one hand and a resulting adjective/adverb on the
other hand:
LE2-4003
EuroWordNet
119
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 17
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Example:
Effect:
LE2-4003
a
Actions and Events resulting in a state denoted by an adjective
Test sentence
als er sprake is van X dan wordt iets/iemand/het Y
(if it is the case that X then something/someone/it becomes Y)
X is a noun or verb denoting an event
Y is an adjective, possibly in a comparative form
als er sprake is van genezen dan wordt iets/iemand/het beter
X = genezen (heal)
Y = beter (better, healthy)
genezen V
CAUSES
beter A
beter A
IS_CAUSED_BY
genezen V
reversed
als er sprake is van activeren dan wordt iets/iemand/het actief
X = activeren (activate)
Y = actief (active)
activeren V CAUSES
actief A
actief A
IS_CAUSED_BY
activeren V reversed
EuroWordNet
120
EuroWordNet D006: Definition of the links and subsets for verbs
In those cases that the resulting state is denoted by a noun a separate test is needed to deal with the
nominal form. Since the nominalization also makes the dynamicity explicit we need a separate test for
nominal results which are static and nominal results which are dynamic:
Dutch test 18
Comment:
Score
yes
a
Actions and Events resulting in a state denoted by a noun
Test sentence
als er sprake is van X dan komt iets/iemand/het in een toestand van Y
(if it is the case that X then something/someone/it gets in a state of Y)
X is a noun or verb denoting an event
Y is an noun denoting a state
als er sprake is van doden dan komt iets/iemand/het in een toestand van dood
X = doden (kill)
Y = dood (death)
doden V
CAUSES
dood N
dood N
IS_CAUSED_BY
doden V
reversed
Conditions:
Example: a
Effect:
Dutch test 19
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Actions and Events resulting in an event denoted by a noun
Test sentence
als er sprake is van X dan vindt Y plaats als gevolg.
(if it is the case that X then Y takes place as a consequence)
X is a noun or verb denoting an event
Y is an noun denoting an event
als er sprake is van doden dan vindt sterfte plaats als gevolg
X = doden (kill)
Y = sterfte (the dying)
doden V
CAUSES
sterfte N
sterfte N
IS_CAUSED_BY
doden V
reversed
Note that the difference in dynamicity between the nouns “sterfte” (the dying) and “dood” (death) is
not expressed by the CAUSE relation but has to follow from the hyponymy-relations with
“verandering” (change) and “toestand” (state), respectively. Another interesting aspect of the Dutch
situation is that “dood” is also an adjective (meaning “dead”) which is synonymous with the noun.
LE2-4003
EuroWordNet
121
EuroWordNet D006: Definition of the links and subsets for verbs
The following tests elicit verb-meronymy relations, which are stored as HAS_SUBEVENT/
IS_SUBEVENT_OF relations. Automatically reversed relations which do not necessarily hold have
again been labelled with the the label reversed:
Dutch test 20
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Subevent relation between verbs using a sub-process construction
Test sentence
om te X moet je Y
(in order to X you have to Y)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
om te kopen moet je betalen
?om te betalen moet je kopen
X = kopen (buy)
Y = betalen (pay)
kopen V
HAS_SUBEVENT
betalen V
betalen V
IS_SUBEVENT_OF
kopen V
reversed
Another way of eliciting subevent relations is by temporal inclusion of events within the embedding
event:
LE2-4003
EuroWordNet
122
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 21
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
Subevent relation between verbs using temporal inclusion
Test sentence
X vindt plaats tijdens/gedurende/onder Y
(X occurs during/while Y)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
bedreigen vindt plaats tijdens/gedurende afpersen
?afpersen vindt plaats tijdens/gedurende het bedriegen
X = bedreigen (threaten)
Y = afpersen (blackmail)
afpersen V
HAS_SUBEVENT
bedreigen V
bedreigen V IS_SUBEVENT_OF
afpersen V
reversed
dromen vindt plaats tijdens/gedurende slapen
?slapen vindt plaats tijdens/gedurende het dromen
X = dromen (dream)
Y = slapen (sleep)
dromen V
IS_SUBEVENT_OF
slapen V
slapen V
HAS_SUBEVENT
dromen V
reversed
In the case of the tempotral inclusion test, the semantic anomaly of the converse sentence b) is not very
strong. To make the relation more explicit this test should be combined with an implicational test such
as the following if-then test:
LE2-4003
EuroWordNet
123
EuroWordNet D006: Definition of the links and subsets for verbs
Dutch test 22
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
LE2-4003
Subevent relation between verbs using if/then implication
Test sentence
als X plaatsvindt dan vindt ook Y plaats
(if X takes place then also Y takes place)
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
als afpersen plaatsvindt dan vindt ook bedreigen plaats
?als bedreigen plaatsvindt dan vindt ook afpersen plaats
X = afpersen (blackmail)
Y = bedreigen (threaten)
afpersen V
HAS_SUBEVENT
bedreigen V
bedreigen V IS_SUBEVENT_OF
afpersen V
reversed
als dromen plaatsvindt dan vindt ook slapen plaats
?als slapen plaatsvindt dan vindt ook dromen plaats
X = dromen (dream)
Y = slapen (sleep)
dromen V
IS_SUBEVENT_OF
slapen V
slapen V
HAS_SUBEVENT
dromen V
reversed
EuroWordNet
124
EuroWordNet D006: Definition of the links and subsets for verbs
Parallel to the meronymy tests for nouns we can use compositional structures for eliciting subevent
relations:
Dutch test 23
Comment:
Score
yes
a
Subevent relation between verbs and nouns using compositional structures
Test sentence
(een/de/het) X bestaat uit (een/de/het) Y en andere gebeurtenissen/handelingen
(an/a/the X consists of an/a/the Y and other events/actions)
the converse of a)
X and Y are dynamic nouns or verbs in the infinitive form
autorijden bestaat uit sturen en andere handelingen
?sturen bestaat uit autorijden en andere handelingen
X = autorijden (drive a car)
Y = sturen (steer)
autorijden V HAS_SUBEVENT
sturen V
sturen V
IS_SUBEVENT_OF
autorijden V reversed
vergaderen bestaat uit besluitvorming en andere handelingen
? besluitvorming bestaat uit vergaderen en andere handelingen
X = vergaderen (to assemble)
Y = opening decison-making)
vergaderen V
HAS_SUBEVENT besluitvorming N
besluitvorming N
IS_SUBEVENT_OF vergaderen V reversed
no
b
Conditions:
Example: a
b
Effect:
Example: a
b
Effect:
The next test elicits a general involvement relation, irrespective of the specific role. The example of
“hamer” (hammer) and “timmeren” (to hammer) shows that specific involvements also pass the general
test:
Dutch test 24
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Involvement/role relation in general
Test sentence
(een) X is degene/dat die/wat bij het Y betrokken is
((a/an) X is the one/that who/which is involved in Ying)
X is a noun
Y is a verb in the infinitive form
een hamer is dat wat bij het timmeren betrokken is
X = hamer (hammer)
Y = timmeren (hammer)
hamer N
ROLE
timmeren V
timmeren V INVOLVED hamer N
reversed
Note that in the case of these role-relations it does not make sense to reverse the test because a verb can
never occupy the position of the concrete noun nor can the concrete noun take the position of the verb.
The b) sentences have therefore been omitted. Furthermore, as with the previous cases we see here that
LE2-4003
EuroWordNet
125
EuroWordNet D006: Definition of the links and subsets for verbs
the reversal of the relation again leads to a generated implication which may not necessarily hold: a
“hamer” (hammer) is not necessarily involved in an event of “timmeren” (hammer). The label reversed
can thus automatically be inserted for the reverse relations of these roles. The next tests can then be
used to elicit more specific roles/involvements.
Dutch test 25
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Example:
a
Effect:
Dutch test 26
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Dutch test 27
Comment:
Score
yes
a
LE2-4003
Agent Involvement/role
Test sentence
(een) X is degene/dat die/wat het Y uitvoert/doet
((a/an) X is the one/that who/which executes/does the Ying)
X is a noun
Y is a verb in the infinitive form
een timmerman is degene die het timmeren uitvoert
(a carpenter is the one who executes the hammering)
X = timmerman (carpenter)
Y = timmeren (hammer)
timmerman N ROLE_AGENT
timmeren V
timmeren V INVOLVED_AGENT
timmerman N reversed
een bedreiging is datgene dat het dreigen doet
(a threat is that which does the threatening)
X = bedreiging (a threat)
Y = dreigen (to threat)
bedreiging N ROLE_AGENT
dreigen V
dreigen V
INVOLVED_AGENT
bedreiging N reversed
Patient Involvement/role
Test sentence
(een) X is degene/dat die/wat het Y ondergaat
((a/an) X is the one/that who/which is undergoing the Ying)
X is a noun
Y is a verb in the infinitive form
een spijker is dat wat het timmeren ondergaat
X = spijker (nail)
Y = timmeren (hammer)
spijker N
ROLE_ PATIENT
timmeren V
timmeren V INVOLVED_ PATIENT
spijker N
reversed
Instrument Involvement/role
Test sentence
(een) X is het instrument waarmee het Y wordt gedaan
((a/an) X is the instrument with which the Ying happens)
EuroWordNet
126
EuroWordNet D006: Definition of the links and subsets for verbs
Conditions:
Example:
a
Effect:
X is a noun
Y is a verb in the infinitive form
(een) hamer is het instrument waarmee het timmeren wordt gedaan
X = hamer (hammer)
Y = timmeren (hammer)
hamer N
ROLE_ INSTRUMENT
timmeren V
timmeren V INVOLVED_ INSTRUMENT
hamer N reversed
For those things which are used as instruments but which cannot be called an “instrument” another test
can be used which avoids the phrase “instrument”:
Dutch test 28
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Dutch test 29
Comment:
Score
yes
a
Conditions:
Example:
Effect:
LE2-4003
a
Instrument Involvement/role without usinmg the phrase “instrument”
Test sentence
(een) X is wat gebruikt wordt voor het Y
((a/an) X is what is used for the Ying)
X is a noun
Y is a verb in the infinitive form
inkt is wat gebruikt wordt voor het schrijven
X = inkt (ink)
Y = schrijven (write)
inkt N
ROLE_ INSTRUMENT
schrijven V
schrijven V INVOLVED_ INSTRUMENT
inkt N reversed
Location Involvement/role
Test sentence
(een) X is de plaats waar het Y plaatsvindt
((a/an) X is the place where the Ying happens)
X is a noun
Y is a verb in the infinitive form
een school is de plaats waar het onderwijzen plaatsvindt
X = school (school)
Y = onderwijzen (teach)
school N
ROLE_ LOCATION
onderwijzen V
onderwijzen V INVOLVED_ LOCATION school N reversed
EuroWordNet
127
EuroWordNet D006: Definition of the links and subsets for verbs

Dutch test 30
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:

Dutch test 31
Comment:
Score
yes
a
Conditions:
Example:
Effect:
LE2-4003
a
Source-Location Involvement/role
Test sentence
(een) X is de plaats waarvandaan het Y plaatsvindt
((a/an) X is the place where from the Ying happens)
X is a noun
Y is a verb in the infinitive form
de start is de plaats waarvandaan het racen plaatsvindt
X = start (start)
Y = racen (race)
start N
ROLE_ SOURCE_ LOCATION
racen V
racen V
INVOLVED_ SOURCE_ LOCATION
start N
reversed
Target-Location Involvement/role
Test sentence
(een) X is de plaats waarnaartoe het Y plaatsindt
((a/an) X is the place where to the Ying takes place)
X is a noun
Y is a verb in the infinitive form
de finish is de plaats waarnaartoe het racen plaatsvindt
X = finish (finish)
Y = racen (race)
finish N
ROLE_ TARGET _ LOCATION
racen V
INVOLVED_ TARGET_ LOCATION
racen V
finish N
reversed
EuroWordNet
128
EuroWordNet D006: Definition of the links and subsets for verbs
The final test elicits properties of events:
Dutch test 32
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Actions and Events being in a state
Test sentence
als er sprake is van X dan gebeurt iets/iemand/het Y
(if it is the case that X then something/someone/it happens Y
X is a noun or verb denoting an event
Y is an adjective, adverb or PP expressing a property of the event
als er sprake is van haasten dan gebeurt iets/iemand/het snel/haastig
X = haasten (hurry)
Y = snel (quick, quickly)
haasten V
BE_IN_STATE
snel A
snel A
STATE_OF
haasten V
reversed
5.1.2 Tests for Italian
In the following specific criteria for identification of relations with respect to Italian are
provided. In some cases, i.e. when tests are either identical to English ones or to similar to other tests
for Italian, we may omit them.
Italian test (1)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Synonymy between verbs
Test sentence
se qlco./qlcu. X allora qlco./qlcu. Y
se qlco./qlcu. Y allora qlco./qlcu. X
X is a verb in the third person singular form
Y is a verb in the third person singular form
there are no specifying PPs that apply to the X-phrase or the Y-phrase
se qlco./qlcu. comincia allora qlco./qlcu. inizia
se qlco./qlcu. inizia allora qlco./qlcu. comincia
synset variants: {cominciare, iniziare}
EuroWordNet
129
EuroWordNet D006: Definition of the links and subsets for verbs
Italian Test (2)
Comment:
Score
yes-probably a
yes/yes
yes-probably b
yes/yes
Conditions:
Italian Test (3)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Near-Synonymy between verbs
Test sentence
se qlco./qlcu. X allora qlco./qlcu. Y
e
i) Xh e Yh sono differenti o ii) Hx e Hy sono differenti
se qlco./qlcu. Y allora qlco./qlcu. X
e
i) Yh e Xh sono differenti o ii) Hy e Hx sono differenti
X and Y are verbs in the third person singular form
X and Y are not linked by any other relation
Xh is the set of the hyponyms (if there are any) of X
Yh is the set of the hyponyms (if there are any) of Y
Hx is the set of the hyperonyms (if there are any) of X
Hy is the set of the hyperonyms (if there are any) of Y
Near-Synonymy between state-denoting verbs and adjectives
Test sentence
se qlco./qlcu. X allora qlco./qlcu. è Y
se qlco./qlcu. è Y allora qlco./qlcu. X
X is a verb in the third person singular form
Y is an adjective
there are no specifying PPs that apply to the X-phrase or the Y-phrase
preferably there is a morphological link between the noun and the verb
se qlco. vive allora qlco. è vivo
se qlco. è vivo allora qlco. vive
{vivere}
XPOS_NEAR_SYNONYM
{vivo}
EuroWordNet
130
EuroWordNet D006: Definition of the links and subsets for verbs
Italian Test (4)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Italian Test (5)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Italian Test (6)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Italian Test (7)
Hyperonymy/hyponymy between verb synsets
Test sentence
X è Y + phrase
Y è X (phrase)
X is a verb in the infinitive form
Y is a verb in the infinitive form
there is at least one specifying AdvP, AdjP, NP or PP that applies to the Y-phrase
correre è andare (velocemente)
*andare è correre (velocemente)
{correre}
HAS_HYPERONYM {andare}
{andare}
HAS_HYPONYM
{correre}
Hyponymy between state-denoting verbs and adjectives
Test sentence
X + phrase è/significa essere Y
essere Y è/significa X
X is a verb in the infinitive form
Y is an adjective
there is at least one specifying AdvP, AdjP, NP or PP that applies to the X-phrase
preferably there is no morphological link between the noun and the verb
Ammalarsi facilmente è essere cagionevole
? Essere cagionevole è ammalarsi
{ammalarsi}
XPOS_HYPERONYM_OF
{cagionevole}
{cagionevole}
XPOS_HYPONYM_OF
{ammalarsi}
Antonymy between verb synsets (a)20
Test sentence
se qlco./qlcu. X allora qlco./qlcu. non Y
se qlco./qlcu. Y allora qlco./qlcu. non X
X is a verb in the third person singular form
Y is a verb in the third person singular form
X and Y are co-hyponyms
se qlcu. ingrassa allora qlcu. non dimagrisce
se qlcu. dimagrisce allora qlcu. non ingrassa
{ingrassare, prendere peso} NEAR_ANTONYM {dimagrire, perdere peso}
20
As discussed in § 2.4, three different antonymy relations can be encoded. However, since the tests developed are all
similar, we are only going to provide translations for the ones for synsets antonymy.
LE2-4003
EuroWordNet
131
EuroWordNet D006: Definition of the links and subsets for verbs
Comment:
Score
yes
a
yes
b
Conditions:
Antonymy between verb synsets (b)
Test sentence
se qlco./qlcu. X allora qlco./qlcu. non Y
se qlco./qlcu. Y allora qlco./qlcu. non X
X is a verb in the third person singular form
Y is a verb in the third person singular form
there is a hyperonym of X which is opposite to a hyperonym of Y
se qlcu. vende allora qlcu. non compra
se qlcu. compra allora qlcu. non vende
{vendere}
NEAR_ANTONYM {comprare}
Example: a
b
Effect:
Italian Test (8)
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Italian Test (9)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Italian Test (10)
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
Italian Test (11)
Comment:
Score
LE2-4003
Antonymy between verb synsets (c)
Test sentence
se qlco./qlcu. X allora qlco./qlcu. non Y
se qlco./qlcu. Y allora qlco./qlcu. non X
X is a verb in the third person singular form
Y is a verb in the third person singular form
X has an addressee and the addressee is the protagonist of Y
se qlcu. dà allora qlcu. non riceve
se qlcu. riceve allora qlcu. non dà
{dare} NEAR_ANTONYM
{ricevere}
Involvement relation in general
Test sentence
un/a X è chi/la cosa che è implicato/a nel Y
X is a noun
Y is a verb in the infinitive form
Un martello è la cosa implicata nel martellare
{martellare} INVOLVED {martello}
Agent Involvement
Test sentence
un/a X è chi/la cosa che esegue l’Y-are ( /Y)
X is a noun
Y is a verb
un insegnante è chi (esegue l’insegnare) insegna
{insegnare} INVOLVED_AGENT
{insegnante}
Patient Involvement
Test sentence
EuroWordNet
132
EuroWordNet D006: Definition of the links and subsets for verbs
yes
a
Conditions:
Example:
Effect:
a
Italian Test (12)
Comment:
Score
yes
a
Conditions:
Example (1):
Effect:
Example (2):
Effect:
Italian Test (13)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Italian Test (14)
Comment:
Score
yes
a
Conditions:
Example:
a
Effect:
LE2-4003
un/a X è chi/la cosa che è sottoposto all’Y-are (/Y)
X is a noun
Y is a verb
un allievo è chi impara (è sottoposto all’imparare)
{imparare} INVOLVED_PATIENT
{allievo}
Instrument Involvement (also valid for nouns indicating things, used for doing
something, which cannot be really called ‘instruments’)
Test sentence
un/a X è lo strumento/ciò che è usato per Y
X is a noun
Y is a verb in the infinitive form
un martello è lo strumento usato per martellare
{martellare} INVOLVED_INSTRUMENT
{martello}
un natante è ciò che è usato per navigare
{navigare}
INVOLVED_INSTRUMENT
{natante}
Location Involvement
Test sentence
un/a X è il luogo dove si Y
X is a noun
Y is a verb in the third person singular form
una scuola è il luogo dove si insegna
{insegnare} INVOLVED_LOCATION {scuola}
Direction Involvement
Test sentence
è possibile Y da o in/ad un luogo
Y is a verb in the infinitive form
è possibile correre da o in un luogo
{correre}
INVOLVED_DIRECTION {direzione}
EuroWordNet
133
EuroWordNet D006: Definition of the links and subsets for verbs
Italian Test (15)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Italian Test (16)
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Italian Test (17)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
LE2-4003
Source-Direction Involvement
Test sentence
il/lo/la X è il luogo dal quale si Y
X is a noun
Y is a verb
la patria è il luogo dal quale si emigra
{emigrare} INVOLVED_SOURCE
{patria}
Target-Direction Involvement
Test sentence
il/lo/la X è il luogo al/nel quale si Y
X is a noun
Y is a verb
la casa è il luogo nel quale si rincasa
{rincasare} INVOLVED_TARGET_DIRECTION
Factive causation relation between verbs
Test sentence
X causa Y
X ha Y come conseguenza
X porta a Y
the converse of (a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
uccidere causa (il) morire
uccidere ha (il) morire come conseguenza
uccidere porta (qualcuno) a morire
*morire causa (l’) uccidere
*morire ha (l’) uccidere come conseguenza
*morire porta (qualcuno) a uccidere
{uccidere}
CAUSES
{morire}
{morire}
IS_CAUSED_BY
{uccidere}
{casa}
factive
reversed
EuroWordNet
134
EuroWordNet D006: Definition of the links and subsets for verbs
Italian Test (18)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Italian Test (19)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
LE2-4003
Non-factive causation relation between verbs using a modal auxiliary
Test sentence
X può causare Y
X può avere Y come conseguenza
X può portare a Y
the converse of (a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
cercare può causare trovare
cercare può avere trovare come conseguenza
cercare può portare a trovare
?trovare può causare cercare
?trovare può avere cercare come conseguenza
?trovare può portare a cercare
{cercare}
CAUSES
{trovare}
(non-factive)
{trovare}
IS_CAUSED_BY
{cercare}
(non-factive)
Has_subevent/Is_subevent_Of relation between verbs (/nouns) (a)
Test sentence
Se X sta avendo luogo allora Y può avere luogo
the converse of a)
X is a verb in the infinitive form
Y is a verb in the infinitive form
Se (il) dormire sta avendo luogo allora (il) russare può aver luogo
* Se (il) russare sta avendo luogo allora (il) dormire può aver luogo
{russare}
IS_SUBEVENT_OF
{dormire}
{dormire}
HAS_SUBEVENT
{russare}
reversed
EuroWordNet
135
EuroWordNet D006: Definition of the links and subsets for verbs
Italian test (20)
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
Has_subevent/Is_subevent_Of relation between verbs (/nouns) (b)
Test sentence
Se Y sta avendo luogo allora X deve aver luogo
the converse of a)
Y is a verb in the infinitive form
X is a verb in the infinitive form
Se (il) comprare sta avendo luogo allora (il) pagare deve aver luogo
* Se (il) pagare sta avendo luogo allora (il) comprare deve aver luogo
{comprare}
HAS_SUBEVENT
{pagare}
{pagare}
IS_SUBEVENT_OF
{comprare} reversed
5.1.3 Tests for Spanish
Spanish test 1
Comment:
Score
yes
a
yes
b
Conditions:
Example:
a
b
Effect:
Spanish test 2
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Synonymy between verbs
Test sentence
Si algo/alguien (se) X, entonces ese algo/alguien (se) Y
Si algo/alguien (se) Y, entonces ese algo/alguien (se) X
X is a verb in the third person singular form
Y is a verb in the third person singular form
there are no specifying PPs that apply to the X-phrase or the Y-phrase
Si algo empieza, entonces ese algo comienza
Si algo comienza, entonces ese algo empieza
synset variants: {comenzar V,empezar V}
Synonymy of nouns and verbs denoting events and states
Test sentence
Si se da el caso de un X, entonces algo/alguien (se) Y
Si algo/alguien (se) Y, entonces se da el caso de un X.
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Si se da el caso de un movimiento, entonces algo se mueve
Si algo se mueve, entonces se da el caso de un movimiento
X = movimiento (movement)
Y = moverse (move)
movimiento N
XPOS_NEAR_SYNONYM
moverse V
moverse V
XPOS_NEAR_SYNONYM
movimiento N
Spanish test 3
LE2-4003
EuroWordNet
136
EuroWordNet D006: Definition of the links and subsets for verbs
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Spanish test 4
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
LE2-4003
Synonymy between event denoting nouns and verbs
Test sentence
Si tiene lugar un X, entonces algo/alguien (se) Y
Si algo/alguien (se) Y, entonces tiene lugar un X
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Si tiene lugar un movimiento, entonces algo/alguien se mueve
Si algo se mueve, entonces tiene lugar un movimiento
X = movimiento (movement)
Y = moverse (move)
movimiento N
XPOS_NEAR_SYNONYM
moverse V
moverse V
XPOS_NEAR_SYNONYM
movimiento N
Synonymy between state-denoting nouns and verbs
Test sentence
Si se da un estado de X, entonces algo/alguien Y
Si algo/alguien Y, entonces se da un estado de X
- X is a noun in the singular
- Y is a verb in the third person singular form
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Si se da un estado de sueño, entonces alguien duerme
Si alguien duerme, entonces se daun estado de sueño
X = sueño (sleep)
Y = dormir (sleep)
sueño N
XPOS_NEAR_SYNONYM
dormir V
dormir V
XPOS_NEAR_SYNONYM
sueño N
EuroWordNet
137
EuroWordNet D006: Definition of the links and subsets for verbs
Spanish test 5
Comment:
Score
yes
a
yes
b
Conditions:
Example: a
b
Effect:
Spanish test 6
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
LE2-4003
Synonymy between state-denoting verbs and adjectives
Test sentence
Si alguien/algo X entonces alguien/algo es/está Y
Si alguien/algo es/está Y entonces algo/alguien X
- X is a verb in the third person singular
- Y is an adjective
- there are no specifying PPs that apply to the X-phrase or the Y-phrase
- preferably there is a morphological link between the noun and the verb
Si alguien teme entonces alguien está temeroso
Si alguien está temeroso entonces alguien teme
X = temer (fear)
Y = temeroso (affraid)
temer V
XPOS_NEAR_SYNONYM
temeroso A
temeroso A XPOS_NEAR_SYNONYM
temer V
Hyperonymy/hyponymy between verbs
Test sentence
Si algo/alguien (se) X, entoces ese algo/alguien (se) Y
Si algo/alguien (se) Y, entonces ese algo/alguien (se) X
X is a verb in the third person singular form
Y is a verb in the third person singular form
there is at least one specifying adverb, NP or PP that applies to the X-phrase or
the Y-phrase
Si alguien corre, entonces ese alguien se mueve (rápidamente)
*Si alguien se mueve, entonces ese alguien corre
EuroWordNet
138
EuroWordNet D006: Definition of the links and subsets for verbs
Spanish test 7
Comment:
Score
yes/no
a
no/yes
b
Conditions:
Example: a
b
Effect:
Spanish test 8
Comment:
Score
yes
a
no
b
Conditions:
Example: a
b
Effect:
LE2-4003
Hyponymy between nouns and verbs
Test sentence
Si se da el caso de un X, entonces es que algo/alguien (se) Y + NP, PP (in a
certain way)
Si algo/alguien (se) Y, entonces se da el caso de un X
- X is a noun in the singular
- Y is a verb in the third person singular form
- there should be at least one specifying NP or PP that makes the Y-phrase
equivalent to the X-phrase or the other way around.
- preferably there is no morphological link between the noun and the verb
Si se da el caso de un asesinato, entonces es que alguien mata (con alevosía)
?Si alguien mata entonces se da el caso de un asesinato
X = asesinato (murder)
Y = matar (kill)
asesinato N
HAS_XPOS_HYPERONYM
matar V
matar V
HAS_XPOS_HYPONYM
asesinato N
Hyponymy between state-denoting verbs and adjectives
Test sentence
X es ser/estar Y (phrase)
ser/estar Y es X (phrase)
X is a verb in the infinitive form
Y is an adjective
there is at least one specifying adverb, NP or PP that applies to the X-phrase or
the Y-phrase
preferably there is no morphological link between the noun and the verb
prevaricar es ser injusto (a propósito al dictar una resolución)
?ser injusto es prevaricar (a propósito al dictar una resolución)
X = prevaricar (pervert the course of justice)
Y = injusto (unjust)
{prevaricar} XPOS_HYPONYM_OF
{injusto}
{injusto}
XPOS_HYPERONYM_OF {prevaricar}
EuroWordNet
139
EuroWordNet D006: Definition of the links and subsets for verbs
Spanish test 9
Comment:
Score
yes
a
yes
b
Conditions:
Example:
a
b
Effect:
Spanish test 10
Comment:
Score
yes
a
b
Conditions:
Example:
a
b
Effect:
LE2-4003
Antonymy between verbs
Test sentence
Que algo/alguien (se) X es lo contrario de que ese algo/alguien (se) Y
Que algo/alguien (se) Y es lo contrario de que ese algo/alguien (se) X
X is a verb in the third person singular form
Y is a verb in the third person singular form
X and Y are co-hyponyms
Que alguien se engorde es lo contrario de que ese alguien adelgace
Que alguien adelgace es lo contrario de que ese alguien engorde
X = engordarse (get fat)
Y = adelgazar (get thin)
engordarse V ANTONYM adelgazar V
adelgazar V ANTONYM engordarse V
Factive causation relation between verbs
Test sentence
Que alguien/algo X causa que alguien/algo Y
Que alguien/algo X tiene como consecuencia que alguien/algo Y
Que alguien/algo X lleva a que alguien/algo Y
the converse of (a)
X is a verb in in the third person singular form of subjunctive
Y is a verb in in the third person singular form of subjunctive
Que alguien/algo asesine causa que alguien/algo muera
Que alguien/algo asesine tiene como consecuencia que alguien/algo muera
Que alguien/algo asesine lleva a que alguien/algo muera
*Que alguien/algo muera causa que alguien/algo asesine
*Que alguien/algo muera tiene como consecuencia que alguien/algo asesine
*Que alguien/algo muera lleva a que alguien/algo asesine
{asesinar}
CAUSES
{morir}
{morir}
IS_CAUSED_BY
{asesinar}
reversed, non-factive
EuroWordNet
140
EuroWordNet D006: Definition of the links and subsets for verbs
Spanish test 11
Comment:
Score
yes
a
no
b
Conditions:
Example:
a
b
Effect:
Spanish test 12
Comment:
Score
yes
a
no
b
Conditions:
Example:
Effect:
a
b
Non-factive causation relation between verbs using a modal auxiliary
Test sentence
Que alguien/algo X puede causar que alguien/algo Y
Que alguien/algo X puede tener como consecuencia que alguien/algo Y
Que alguien/algo X puede llevar a que alguien/algo Y
the converse of (a)
X is a verb in in the third person singular form of subjunctive
Y is a verb in in the third person singular form of subjunctive
Que alguien/algo busque puede causar que alguien/algo encuentre
Que alguien/algo busque puede tener como consecuencia que alguien/algo
encuentre
Que alguien/algo busque puede llevar a que alguien/algo encuentre
?Que alguien/algo encuentre puede causar que alguien/algo busque
?Que alguien/algo encuentre puede tener como consecuencia que alguien/algo
busque
?Que alguien/algo encuentre puede llevar a que alguien/algo busque
{buscar}
CAUSES
{encontrar} non-factive
{encontrar} IS_CAUSED_BY
{buscar}
reversed, non-factive
Subevent relation between verbs
Test sentence
Para X tienes que Y
Para Y tienes que X
X is a verb infinitive
Y is a verb in infinitive
X and Y are not connected by a hyponymy/hyperonymy relation
Para comprar tienes que pagar
*Para pagar tienes que comprar
{comprar}
HAS_SUBEVENT {pagar}
{pagar}
IS_SUBEVENT_OF {comprar}
Verb test 13
Comment:
Involvement and Role relation in general
This test is replaced by accomplishing one of the specific Involvement tests below (14 to 20)
LE2-4003
EuroWordNet
141
EuroWordNet D006: Definition of the links and subsets for verbs
Spanish test 14
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Spanish test 15
Comment:
Score
yes
a
Conditions:
Example:
Effect:
a
Spanish test 16
Comment:
Score
yes
a
Conditions:
Example:
Effect:
LE2-4003
Agent Involvement/Role
Test sentence
Un X es un X porque es aquel/aquello que Y
X is a noun
Y is a verb in present third person singular form
Un educador es un educador por que es aquel que educa
{educador} ROLE_AGENT
{educar}
{educar}
INVOLVED_AGENT
{educador}
reversed
Patient Involvement/Role
Test sentence
Un X es un X porque es aquel/aquello que es/puede ser/ha (sido) Y-do
X is a noun
Y is a verb in participle form
Un educando es un educando porque es aquel que es educado
{educando} ROLE_PATIENT
{educar}
{educar}
INVOLVED_PATIENT
{educando} reversed
Instrument Involvement/Role (also valid for nouns indicating things, used for
doing something, which cannot be really called ‘instruments’)
Test sentence
Un X es un X porque es una cosa que sirve para Y
X is a noun
Y is a verb in the infinitive form
Un martillo es un martillo porque sirve para martillear
{martillo}
ROLE_INSTRUMENT
{martillear}
{martillear} INVOLVED_INSTRUMENT
{martillo}
reversed
EuroWordNet
142
EuroWordNet D006: Definition of the links and subsets for verbs
5.2 Data obtained by the analysis performed on Dutch
5.2.1 List of Base Concepts
beinvloeden
00435835-v
1 2629
influence
24
7
VlisNoEnd
01436060-v
aanbieden
01307317-v
2 2511
offer
7
7
VlisEnd
aanbrengen
00783910-v
1 2082
puton
25
6
VlisNoEnd
bedenken
00935525-v
2 3700
think up
13
2
VlisEnd
00396499-v
beginnen
00207968-v
6 36461
begin
16
1
VlisNoEnd
begrijpen
00330150-v
1 18184 15
understand 1
VlisNoEnd
behandelen
00048767-v
5 4787
treat
17
2
VlisNoEnd
bekijken
01216027-v
1 5688
look
15
8
VlisNoEnd
bepalen
00393722-v
1 13245
determine
15
2
VlisNoEnd
00428728-v
beschermen
00639004-v
1 3091
protect
18
1
VlisNoEnd
beschouwen
00388394-v
2 7764
consider
23
1
VlisNoEnd
betalen
01281885-v
2 5332
pay 4
30
VlisNoEnd
bevinden
01501697-v
2 5376
be 9
6
VlisEnd
bevinden
01501697-v
3 5376
be 9
14
VlisEnd
bewegen
01043075-v
1 6157
move
23
14
VlisNoEnd
01046072-v
LE2-4003
influence
8
think up
1
determine
5
move
15
EuroWordNet
143
EuroWordNet D006: Definition of the links and subsets for verbs
bewegen
01055491-v
2 6157
move
20
16
VlisNoEnd
bewegen
01046072-v
6 6157
move
15
15
VlisNoEnd
binnenkomen 1 2295
01152122-v enter
17
5
VlisNoEnd
blijven
00068138-v
5 52837
remain
1
1
VlisEnd
brengen
00078946-v
4 36536
bring
5
1
VlisEnd
00827521-v
bring
5
controleren
01226798-v
2 2087
check
21
25
VlisNoEnd
01421427-v
check
28
dansen
00978339-v
2 2919
dance
17
4
VlisNoEnd
delen
00897572-v
2 3233
divide
16
3
VlisNoEnd
01161526-v
divide
5
denken
00341396-v
3 58538
think
8
2
VlisEnd
denken
00387631-v
4 58538
think
9
6
VlisEnd
doden
00758542-v
1 2496
kill 5
16
VlisNoEnd
doen
00980842-v
5 71277
do 6
20
VlisEnd
dragen
01537537-v
3 11963
carry
1
27
VlisEnd
drijven
01935511-a
3 3360
soaked
1
1
VlisEnd
drukken
01067117-v
2 6555
push
4
8
VlisEnd
eindigen
01475351-v
2 2041
end 16
2
VlisEnd
00211850-v end 14
ervaren
01008772-v
2 3633
24
experience 6
LE2-4003
VlisNoEnd
01203891-v
experience 7
01434123-v
check
29
01204902-v
experience 8
EuroWordNet
144
EuroWordNet D006: Definition of the links and subsets for verbs
eten
00662381-v
3 8283
eat 2
gaan
01513147-v
1 137589 11
concern 6
VlisEnd
gaan
01046072-v
3 137589 43
go 14
VlisNoEnd
gaan
01100714-v
5 137589 7
head
25
VlisEnd
gaan
00089026-v
8 137589 3
go 4
VlisEnd
gebeuren
00204516-v
2 16006
happen
17
1
VlisEnd
00206903-v
gebruiken
00658243-v
1 17227
use 8
43
VlisEnd
gedragen
00007021-v
3 2091
behave
18
1
VlisNoEnd
01425745-v
gelden
01513610-v
3 8411
apply
6
10
VlisEnd
geven
01255335-v
3 54472
give
1
18
VlisEnd
geven
01271194-v
4 54472
give
29
19
VlisNoEnd
gooien
00867132-v
1 4594
throw
39
9
VlisNoEnd
halen
00823804-v
1 17748
fetch
16
1
VlisNoEnd
01279432-v
handelen
01341700-v
2 4414
act 12
29
VlisEnd
hebben
01257491-v
1 474965 15
have
13
VlisNoEnd
hebben
01487374-v
2 474965 2
have
19
VlisEnd
hebben
00041140-v
3 474965 2
have
3
VlisEnd
helpen
1
VlisNoEnd
LE2-4003
11749
33 VlisNoEnd
00663538-v eat 3
22
00670058-v
eat
4
happen
2
00207186-v
happen
3
behave
2
01426576-v
behave
3
EuroWordNet
145
EuroWordNet D006: Definition of the links and subsets for verbs
01442355-v
help
7
herhalen
00206373-v
1 2853
repeat
20
2
VlisNoEnd
hoeven
00675532-v
1 10197
require
1
3
VlisEnd
houden
01517254-v
8 46508
keep
1
17
VlisEnd
kapotmaken
00900879-v
1 60
destroy
16
3
VlisEnd
00928634-v
kennen
00333177-v
1 21514
know
1
2
VlisEnd
kiezen
00379073-v
1 7555
choose
17
1
VlisNoEnd
kijken
01216027-v
1 51564
look
25
8
VlisNoEnd
klinken
01241976-v
1 8091
sound
48
13
VlisNoEnd
komen
01144761-v
1 135035 33
come
7
VlisNoEnd
01054590-v
kopen
01259481-v
1 6942
buy
15
3
VlisNoEnd
krijgen
01260836-v
1 45929
receive
21
9
VlisNoEnd
00307705-v
kunnen
01539155-v
2 209812 6
can 8
VlisEnd
lachen
00020446-v
1 12773
laugh
20
3
VlisNoEnd
leggen
00859635-v
1 18068
lay 3
16
VlisNoEnd
leiden
01518088-v
2 10955
lead
3
23
VlisEnd
leiden
01381333-v
4 10955
lead
18
21
VlisNoEnd
leven
01477879-v
8 26762
live2
1
VlisEnd
LE2-4003
destroy
4
come
6
receive
2
EuroWordNet
146
EuroWordNet D006: Definition of the links and subsets for verbs
lezen
00351672-v
1 12834
read
22
6
VlisEnd
liggen
00890801-v
1 40957
lie 4
16
VlisNoEnd
lijken
01217877-v
2 24119
seem
15
1
VlisNoEnd
lopen
01084973-v
1 35539
walk
62
11
VlisNoEnd
loslaten
00719229-v
1 1003
20
come off 2
VlisEnd
maken
00926361-v
2 86305
make
49
13
VlisNoEnd
noemen
00581359-v
1 18351
call 20
6
VlisEnd
00547315-v call 19
onderzoeken
00442867-v
2 4017
34
investigate 2
VlisNoEnd
ontbreken
01488598-v
1 3849
lack
5
3
VlisEnd
ontstaan
01484012-v
1 10944
become
16
2
VlisNoEnd
openen
00772933-v
3 4849
open
37
8
VlisEnd
opnemen
00111108-v
7 4801
include
15
1
VlisNoEnd
opvallen
01511883-v
1 2190
excel
9
1
VlisEnd
overeenkomen 1 737
19
01503041-v correspond 3
VlisEnd
overtreffen
00624716-v
1 558
exceed
16
1
VlisEnd
01509366-v
passen
01504707-v
1 6194
fit 10
4
VlisEnd
plaatsen
00859635-v
1 3183
place
44
23
VlisNoEnd
LE2-4003
exceed
2
01509494-v
exceed
3
EuroWordNet
147
EuroWordNet D006: Definition of the links and subsets for verbs
praten
00542186-v
4 14570
talk10
6
VlisEnd
proberen
01432563-v
2 17811
try 9
15
VlisEnd
raken
00089026-v
1 9419
become
9
1
VlisEnd
raken
00806352-v
2 9419
hit 15
12 VlisEnd
00704074-v hit 13
regelen
00942868-v
1 2497
arrange
18
6
VlisEnd
rijden
01114042-v
2 8591
ride
27
8
VlisNoEnd
roepen
04737713-n
1 15111
cry 5
25
VlisNoEnd
schrijven
00559904-v
3 17958
write
13
1
VlisEnd
slaan
00806352-v
1 13752
hit 15
12
VlisEnd
slaan
00809580-v
6 13752
hit 16
27 VlisNoEnd
00806352-v hit 15
slapen
00009337-v
1 8739
sleep
17
4
VlisNoEnd
spelen
00605818-v
5 16115
play
54
21
VlisNoEnd
spreken
00530290-v
2 25142
speak
50
1
VlisNoEnd
springen
01118288-v
1 5030
jump
28
14
VlisNoEnd
steken
00700678-v
3 10772
stab
2
2
VlisEnd
sterven
00216283-v
1 6524
die 6
23
VlisNoEnd
stoppen
00211850-v
8 4691
stop
6
12
VlisEnd
sturen
2
22
VlisEnd
LE2-4003
4284
EuroWordNet
148
EuroWordNet D006: Definition of the links and subsets for verbs
00548636-v
00826451-v
send
send
toenemen
00093597-v
1 3168
25
increase 7
VlisEnd
tonen
00373148-v
2 5565
show
VlisNoEnd
00573040-v
uitvoeren
00981680-v
4 3650
8
perform 2
vallen
01122509-v
1 26001
fall 15
19 VlisNoEnd
01558020-v fall 26
vallen
01122509-v
2 26001
fall 15
26
varen
01053250-v
1 2550
sail 3
24 VlisNoEnd
01108062-v sail 6
vastmaken
00768642-v
1 240
fasten
50
3
vaststellen
00287933-v
00517007-v
1 3381
24 VlisNoEnd
measure 9 00392710-v determine
determine 6
veranderen
00064108-v
1 10109
change
117 VlisEnd
11
veranderen
00071241-v
4 10109
change
101 VlisEnd
12
verbergen
01224184-v
1 3117
hide
9
5
VlisEnd
verbergen
01224184-v
3 3117
hide
5
5
VlisEnd
verbinden
00778333-v
1 2923
connect
18
4
VlisNoEnd
verdwijnen
00253904-v
1 10596 36
disappear 1
VlisNoEnd
verkopen
01546360-v
1 4326
sell 7
1
VlisEnd
verkopen
01277199-v
2 4326
sell 4
23 VlisNoEnd
00430622-v sell 2
LE2-4003
1
5
16
6
00583238-v
01110654-v
send
send
2
6
show
11
00602249-v
send
3
00393722-v
determine 2
VlisEnd
VlisEnd
VlisEnd
1
EuroWordNet
149
EuroWordNet D006: Definition of the links and subsets for verbs
verlangen
01028208-v
2 4070
long
15
1
VlisNoEnd
01042002-v
long
2
verliezen
01301277-v
1 6852
lose
11
7
VlisEnd
01302104-v
lose
10
vermoorden
00125250-n
1 2026
murder
22
1
VlisNoEnd
veroorzaken
00941367-v
1 4285
cause
25
7
VlisEnd
verschijnen
00251107-v
1 7396
appear
16
2
VlisNoEnd
vertrouwen
00386671-v
2 3436
trust
10
8
VlisEnd
00406425-v
trust
9
verwachten
00405636-v
1 8345
expect
6
4
VlisEnd
verwijderen
00104355-v
2 2076
remove
77
2
VlisNoEnd
verzamelen
00794237-v
2 2618
collect
29
2
VlisEnd
00796914-v
collect
3
verzetten
00631049-v
2 1265
resist
17
2
VlisEnd
vliegen
01104809-v
1 4232
fly 15
16
VlisNoEnd
voorstellen
00469225-v
3 5177
15 VlisNoEnd
represent 2 00556972-v represent
3
voorzien
00671827-v
3 2301
provide
76
2
VlisEnd
01323715-v
provide
3
vormen
00949570-v
1 11573
form
11
14
VlisEnd
00083270-v
form
12
vragen
00504861-v
2 34058
ask 4
6
VlisEnd
vragen
00422854-v
3 34058
ask 1
12
VlisEnd
vullen
00268884-v
1 3245
fill 5
18
VlisNoEnd
LE2-4003
EuroWordNet
150
EuroWordNet D006: Definition of the links and subsets for verbs
waarnemen
01202814-v
2 2468
10
perceive 1
VlisEnd
wachten
01492762-v
1 14839
wait
4
5
VlisEnd
01494897-v
weggaan
01147140-v
1 2070
leave
52
10
VlisNoEnd
weigeren
00258338-v
1 3480
fail 2
6
VlisEnd
weigeren
00448333-v
2 3480
refuse
2
weigeren
01274026-v
3 3480
refuse
2
4
VlisEnd
werken
01364691-v
1 17080
work
29
20
VlisNoEnd
01366212-v
weten
00333362-v
2 66735
know
4
3
VlisEnd
willen
01040073-v
1 76697
want
10
8
VlisEnd
winnen
00620486-v
1 3383
win
8
3
VlisEnd
zeggen
00569629-v
1 134802 37
say 8
VlisNoEnd
zetten
00862576-v
1 17652 17
put down 4
VlisNoEnd
zijn
01471536-v
2 852027 5
be 3
VlisEnd
zijn
01501697-v
3 852027 15
be 9
VlisNoEnd
zijn
01472320-v
7 852027 4
be 4
VlisEnd
zingen
00989166-v
1 4234
sing
16
3
VlisEnd
00990063-v
zitten
00888240-v
1 45810
sit 1
16
VlisNoEnd
zorgen
2
9
VlisEnd
LE2-4003
7608
wait
6
work
21
sing
4
VlisEnd
EuroWordNet
151
EuroWordNet D006: Definition of the links and subsets for verbs
01443838-v
care for
5
01443257-v
see to
zullen
01539804-v
2 221958 2
shall
1
zwijgen
00273772-v
1 5191
4
VlisEnd
fall silent 1 01249616-v fall silent
aanduiden
00433815-v
1 1996
16
indicate 1
Level01
aanpassen
00181048-v
2 1060
adapt
17
2
Level01
aanraken
00685874-v
1 1175
touch
21
17
Level01
00686113-v
aanrichten
00941367-v
1 395
2
give rise to 1
Level01
aansporen
00429831-v
1 247
urge
26
2
Level01
aanstellen
01356031-v
1 450
appoint
16
2
aanvallen
00633037-v
2 686
attack
1
VlisEnd
2
touch
18
Level01
01401683-v
appoint
3
19
13
Level01
00633619-v
attack
14
achteruitgaan 2 104
00122638-v decline
24
5
Level01
afhalen
00104355-v
3 424
take
26
4
Level01
afkraken
00462569-v
1 21
15 Level01
criticize harshly 1
afmaken
00212590-v
2 703
finish
29
6
finish
8
afnemen
01318941-v
6 1557
rob 1
15 Level01
01320281-v rob 2
afsluiten
00772512-v
2 1102
close
15
5
Level01
afvoeren
00268395-v
1 260
drain
9
6
Level01
beeindigen
00213455-v
1 802
end 15
35
Level01
LE2-4003
Level01
00285198-v
EuroWordNet
152
EuroWordNet D006: Definition of the links and subsets for verbs
bedekken
00763269-v
1 1275
cover
43
16
Level01
bedriegen
00479186-v
1 692
deceive
19
1
Level01
01456537-v
beginnen
00207968-v
3 36461
begin
11
1
Level01
bekendmaken 1 113
18
00242637-v makepublic 1
Level01
00549256-v
bekrachtigen
01403135-v
1 171
14
declarevalid 1
Level01
belemmeren
01447000-v
1 678
hinder
16
3
Level01
benadelen
01422880-v
1 175
15
disadvantage
Level01
3
beoordelen
00547761-v
1 1668
judge
10
6
Level01
00377820-v
beoordelen
00376571-v
2 1668
judge
21
2
Level01
berekenen
00358556-v
1 737
calculate
15
1
Level01
beschadigen
00154558-v
1 371
damage
15
6
Level01
besteden
01531792-v
1 2795
spend
14
3
Level01
bestrijken
01192082-v
2 435
spread
17
15
Level01
01519515-v
betekenen
00537777-v
1 11718
mean
14
6
Level01
bevestigen
00132823-v
2 2955
confirm
4
1
Level01
bewerken
00302695-v
1 935
treat
90
3
Level01
bezighouden
01367366-v
3 1599
occupy
21
3
Level01
01366212-v
LE2-4003
deceive
2
announce
2
judge
5
spread
16
exert oneself
00549373-v
announce 3
2
EuroWordNet
153
EuroWordNet D006: Definition of the links and subsets for verbs
bezorgen
00941367-v
2 1756
cause
11
7
Level01
brengen
00823804-v
1 36536
bring
13
2
Level01
00824200-v
dalen
01122509-v
1 2332
11
go down 3
Level01
denken
00354465-v
1 58538
think
13
4
Level01
denken
00341396-v
3 58538
think
8
2
Level01
dichtmaken
00772512-v
1 38
close
35
5
Level01
doodgaan
00216283-v
1 585
die 6
3
Level01
doorgaan
00210630-v
3 1875
40 Level01
continue 2 01517254-v continue
dwingen
01418102-v
2 3451
force
13
16
Level01
ergeren
01018552-v
2 1196
irritate
13
2
Level01
gebruiken
00656714-v
2 17227 11
consume 2
Level01
goedkeuren
00452960-v
2 737
agree
12
2
Level01
haasten
01177978-v
1 987
hurry
11
7
Level01
01175685-v
handhaven
01515519-v
1 1943
15
maintain 9
Level01
houden
01515519-v
i
nformeren
00467082-v
9 46508
keep
13
16
Level01
2 1169
inform
9
2
Level01
inspannen
01343200-v
3 400
exert
9
3
Level01
01343364-v
instemmen
1
13
Level01
LE2-4003
395
bring
3
7
hurry
6
overexert
1
EuroWordNet
154
EuroWordNet D006: Definition of the links and subsets for verbs
00452960-v
agree
2
kaarten
00652908-v
1 165
play
25
24
Level01
kalmeren
01005235-v
1 810
calm
6
4
Level01
kapotgaan
00201526-v
1 24
break
23
20
Level01
kletsen
00587229-v
2 594
chaffer
21
1
Level01
kletsen
00586278-v
4 594
15
twaddle 2
Level01
leegmaken
00267845-v
1 43
empty
30
4
Level01
losmaken
00104355-v
1 844
remove
29
2
Level01
meedelen
00467082-v
2 596
inform
16
2
Level01
meedoen
01387083-v
1 716
14
participate 2
Level01
meemaken
01204902-v
1 2062
10
experience 8
Level01
merken
00297919-v
2 9381
mark
14
14
Level01
mislukken
01431735-v
1 1412
fail 7
13
Level01
musiceren
00986807-v
1 83
play
22
34
Level01
naderen
01174613-v
1 2204
14
approach 11
Level01
nadoen
00995557-v
1 121
copy
16
6
Level01
01512597-v
omgaan
01378917-v
3 1475
manage
10
1
Level01
omgeven
01531353-v
1 848
13
surround 6
Level01
LE2-4003
copy
8
EuroWordNet
155
EuroWordNet D006: Definition of the links and subsets for verbs
omhooggaan
01121367-v
1 36
rise 17
18
Level01
omvallen
01122509-v
1 253
fall 15
19
Level01
ontdoen
00117617-v
1 832
strip
59
9
Level01
00104355-v
opengaan
00773597-v
1 485
open
17
9
Level01
opgeven
00611702-v
00611702-v
1 1578
quit
give up
15
1
2
Level01
01269413-v
ophouden
00211850-v
1 2715
stop
11
12
Level01
opschrijven
00576052-v
1 730
16
writedown 1
Level01
optillen
00095155-v
1 399
lift 9
17 Level01
01121367-v lift 25
ordenen
00416049-v
1 637
arrange
15
2
Level01
protesteren
01427604-v
1 988
protest
14
5
Level01
00512172-v
protest
3
samenkomen 1 163
01374778-v meet
16
10
Level01
01408129-v
meet
11
schoonmaken 1 676
00023287-v clean
00881979-v clean
52
2
7
Level01
00106393-v
clean
4
schoppen
00788650-v
2 983
kick
24
6
Level01
spelen
00987477-v
8 16115
play
7
35
Level01
stoppen
00218979-v
5 4691
stop
13
13
Level01
01061046-v
stop
16
streven
01433327-v
3 2147
strive
13
2
Level01
strijden
1
5
Level01
LE2-4003
845
remove
2
00721752-v
strip
14
give up
5
01515268-v
give up
12
00109110-v
clean
5
EuroWordNet
156
EuroWordNet D006: Definition of the links and subsets for verbs
00615347-v
fight
5
toevoegen
00110396-v
1 1737
add 1
16 Level01
00580469-v add 4
uitdoen
00868381-v
2 170
turn off
6
1
uiteenvallen
00237247-v
1 226
10
break intoparts
Level01
1
uiten
00554586-v
1 1412
utter
20
3
Level01
00529407-v
uiten
00531321-v
2 1412
express
20
6
Level01
uitleggen
00528672-v
1 1807
explain
11
2
Level01
uitoefenen
00662183-v
2 2237
exert
3
1
Level01
01257388-v
uitstellen
01495560-v
1 538
14
postpone 1
Level01
uitzoeken
00357043-v
3 577
solve
13
1
Level01
vastbinden
00768642-v
1 413
fix 11
14
Level01
vastleggen
00563886-v
3 1392
15
putdown 1
Level01
vastzitten
01045270-v
1 307
be fixed 1
10 Level01
00131922-a stuck
vechten
00615347-v
1 2651
fight
14
5
Level01
verbeteren
00123459-v
1 1762
improve 1
8
Level01
verbeteren
00123997-v
2 1762
improve 2
18
Level01
verdwijnen
00253904-v
1 10596
disappear
36
Level01
vergissen
00347169-v
1 1304
err 1
15
Level01
LE2-4003
Level01
utter
1
exert
2
1
EuroWordNet
157
EuroWordNet D006: Definition of the links and subsets for verbs
verkleinen
00262983-v
1 337
8
makesmaller 2
Level01
verknoeien
01431414-v
1 288
botch
19
2
Level01
11
Level01
verminderen
00090574-v
1 1804
22
decrease 5
Level01
verminderen
00262983-v
2 1804
23
decrease 6
Level01
vernielen
00900879-v
1 507
destroy
20
3
Level01
vernietigen
00900879-v
1 1876
destroy
17
3
Level01
veroorzaken
00432532-v
1 4285
cause
25
6
Level01
00941367-v
verplaatsen
01055491-v
1 1166
displace 3
43
Level01
verschaffen
01323715-v
1 1982
supply
18
6
Level01
versieren
00959417-v
1 919
32
decorate 2
Level01
verspreiden
00792958-v
1 1781
spread
Level01
00546238-v
vervoeren
00834152-v
1 702
11 Level01
transport 7 01110654-v transport
9
verwarmen
00222990-v
1 702
heat
10
7
Level01
00223859-v
warm
1
verwerven
01301819-v
1 1960
gain
18
9
Level01
verwonden
00043545-v
1 187
injure
20
1
Level01
verzorgen
00048767-v
1 1451
care for
17
1
Level01
00026120-v
neaten
1
vermeerderen 2 464
00093597-v increase 7
LE2-4003
15
11
cause
7
distribute
1
01112683-v
transport
10
EuroWordNet
158
EuroWordNet D006: Definition of the links and subsets for verbs
voorbereiden 1 1808
00241323-v prepare
7
2
Level01
voorbijgaan
01172741-v
22
3
Level01
1 1015
go by
voortbewegen 2 260
50 Level01
01136714-v moveon 2 01046072-v locomote
vrijen
00820052-v
2 882
37
make love 1
Level01
weergeven
00966090-v
1 1051
12
represent 5
Level01
werven
01275844-v
1 167
recruit
14
4
Level01
zien
01215314-v
4 98375
see 14
12
Level01
zuiveren
00881979-v
1
346920 13
makeclean 1
Level01
LE2-4003
1
EuroWordNet
159
EuroWordNet D006: Definition of the links and subsets for verbs
5.3 Data obtained by the analysis performed on Italian
5.3.1 List of Base Concepts
In the following, the results obtained so far are reported (cf. above). In particular we indicate: 1)
the verb homograph; 2) the preliminarily established correspondences among the genus senses and WN
1.5 synsets; 3) the levels in WN taxonomies; 4) the levels in our (preliminary) taxonomies; 5) the
frequency of each non-disambiguated genus (i.e., of its senses altogether). Note that: i) some Italian
verb senses which, for the time being, have not been connected, match with the same WN synset, thus,
either a) we shall insert them in a same synset in a further stage of our work (i.e., when the sense
disambiguation will be complete); or b) we shall state that in Italian more granular distinctions are
operated with respect to those found in WN; ii) when either two WN synsets match with one Italian
verb sense or vice versa, we indicate it by means of the conjunction ‘and’ connecting the two senses
corresponding to one sense in the other language (i.e., WN1 and WN2 = Italian verb, and vice versa).
Top-concept
WN corresponding synsets
Level
in WN
Level
in
Italian
Frequency
as a genus
fare
Sense 1 of to do 01448761
Sense 3 of to do 01449587
Sense 3 of to make 00926361
Sense 1 of to put 00859635
???
Sense 3 of to give 01254390
Sense 1 of to give 01317872
Sense 1 of to provide 01323715
Sense 1 of to become 00089026
Sense 1 of to be 01472320
Sense 1 of to take 00691086
Sense 1 of to become 00089026
Sense 29 of to take 01471041
Sense 10 of to take 01354977
Sense 1 of to have 01256853
Sense 1 of to feel 01008772 and
Sense 3 of to feel 01202814
Sense 1 of remove 00104355
Sense 1 of to lose 01301401
? Sense 1 of to go 01046072
Sense 1 of to go 01046072 and
sense 2 of to go 201054314
? Sense 1 of to change 00072540
Sense 3 of to pass 01172741
Sense 1 of to send 01110956
Sense 1 of to subject 00632326
top
top-5
top
top-1
top-1
top
top-1
top
top
top
top
top-1
top-1
top
top
top
top
top
top
top
top
top
top-1
top-1
top-1
top
top-1
top
top
top
top-1
top-1
top
top
top
top
top
top-1
top
top
top
374
top
top-1
top-1
top-1
135
110
107
top
top-1
top
top-1
92
80
76
74
mettere
dare
diventare
essere
prendere
avere
togliere
perdere
andare
ridurre
passare
mandare
sottoporre
LE2-4003
288
286
221
206
164
163
EuroWordNet
160
EuroWordNet D006: Definition of the links and subsets for verbs
privare
colpire
liberare
portare
cadere
dire
mettersi
parlare
emettere
provare
rendere
tenere
unire
porre
produrre
disporre
divenire
provocare
chiudere
compiere
eseguire
tagliare
coprire
lavorare
dividere
lasciare
cercare
muovere
stare
apparire
trasformare
stabilire
battere
muoversi
esporre
LE2-4003
Sense 1 of to deprive 01316712
Sense 2 of to hit 00704074
Sense 1 of to strike 01007544
Sense 1 of to free 01370323
Sense 1 of to bring 01188762
Sense 2 of to bring 00827521
Sense 1 of fall down 01044689
Sense 2 of to fall 01122509
Sense 4 of to fall 01558020
Sense 1 of to say 00569629 and
sense 8 of to say 00532392
???
Sense 1 of to speak 00530290
Sense 1 of to emit 00062533
Sense 2 of to emit 01563115
Sense 3 of to emit 00554586
Sense 1 of to feel 01008772 and
sense 3 of to feel 01202814
Sense 3 of to make 00926361
Sense 1 of to keep 01515519
Sense 2 of to keep 01517254
Sense 3 of to unite 01483441
Sense 1 of to put 00859635
Sense 6 of to produce 01222100
Sense 1 of to put 00859635
Sense 1 of to to become 00089026
Sense 1 of to provoke 01003070
Sense 1 of to close 00772512
Sense 1 of to accomplish 00938602
Sense 1 of to accomplish 00938602
Sense 1 of to cut 00894185
Sense 1 of to cover 00763269
Sense 3 of to cover 00687390
Sense 1 of to work 01366212
Sense 1 of to divide 01396914
Sense 4 of to divide 00898017
Sense 1 of to distinguish 00365740
Sense 5 of to let 00291924
Sense 1 of to try 01432563
Sense 1 of to look for 00754445
Sense 1 of to move 01046072
Sense 3 of to move 01043075
Sense 1 of to stay 00068138
Sense 2 of to stay 01060041
Sense 1 of to appear 01217877
Sense 2 of to transform 00229041
Sense 3 of to establish 00373148
Sense 2 of to hit 00704074
Sense 1 of to move 01046072
Sense 1 of to show 01225799
top-1
top-1
top
top
top-1
top-1
top-1
top
top-1
top-1
top-1
top-1
top-1
top
top
top
top
top
top
top-1
top
top-1
top-1
top-1
top
top-1
top
top-1
top-1
top-1
top
top-1
top
top-1
top-1
top-1
top-1
top-1
top
top
top
top-1
top-1
top-1
top-1
top-1
top-1
top
top-1
top-1
top
top-1
top-1
top-1
top-1
top-2
70
68
top
53
top
top-1
top-1
52
50
48
top
48
top
top-1
48
45
top-1
top
top-1
top
top
top-1
top
top
top
top-2
top-2
44
43
42
41
40
40
39
39
37
37
36
top
top
top
top
top-1
top-1
36
35
top
top-1
top
34
top
top-1
top-1
top
top
top-1
32
31
30
29
29
28
64
60
54
35
34
33
EuroWordNet
161
EuroWordNet D006: Definition of the links and subsets for verbs
farsi
mostrare
cambiare
esprimere
raccogliere
riconoscere
separare
sostenere
stringere
subire
rompere
volgere
cessare
diminuire
esercitare
indurre
manifestare
scrivere
tirare
condurre
dichiarare
eccitare
aumentare
impedire
riempire
causare
LE2-4003
Sense 1 of to become 00089026
Sense 2 of to cause 00432532
Sense 1 of to behave 00007021
Sense 4 of to show 01219939
Sense 5 of to show 01226169
Sense 1 of to change 00072540
Sense 2 of to change 00064108
Sense 1 of to express 00531321
Sense 1 of to collect 01311458
Sense 3 of to recognize 01251230
Sense 1 of to admit 00459649
Sense 1 of to declare 00570287
Sense 4 of to divide 00898017
Sense 1 of to distinguish 00365740
Sense 1 of to support 01446559
Sense 4 of to support 00693130
Sense 1 of to tighten 00249759
Sense 7 of to press 00799016
Sense 1 of to undergo 01203891
Sense 3 of to break 00787971 and
sense 5 of to break 00201902
Sense 1 of to violate 01509094 and
sense 2 of to violate 01451879
Sense 1 of to turn 01086483
Sense 1 of to stop 01515268
Sense 1 of to lessen 00090574
top-2
top-1
top
top
top
top
top
top-4
top-3
top
top-2
top-2
top-1
top-1
top
top
top-1
top-2
top
top-1
top-1
top
top-4
top-1
top
top-2
Sense 1 of to use 00658243
Sense 1 of to do 01448761
Sense 2 of to induce 00432532
Sense 4 of to show 01219939 and
sense 5 of to show 01226169
Sense 1 of to write 00972859
Sense 1 of to throw 00867132
Sense 1 of to lead 01141779
Sense 5 of to lead 01381333
Senses 1 of to declare 00570287 and
sense 2 of to declare 00544887
Sense 6 of to excite 01004175
Sense 1 of to increase 00093597
Sense 2 of to increase 00091455
Senses 1 of to prevent 01388675 and
sense 2 of to prevent 01387332
Sense 1 of to fill 00268884
Sense 1 of to cause 00941367 and
sense 2 of to cause 00432532
top
top
top
top
top
top-2
top-2
top
top-3
top-2
top-2
top-2
top-2
top-1
top
top-1
top-1
top
top
top
top
top-1
28
top
27
top-1
top-1
top-1
top
top-1
top
top
top-1
26
26
26
top-2
top-1
top
top-2
25
28
26
25
25
24
top-1
top-1
top
top-1
and
top-1
top
top
top-1
top-1
24
23
23
top-2
top-1
top-2
top
top-1
23
23
22
top-1
top-1
top-1
top-1
22
21
top-1
top
21
20
23
23
23
22
21
EuroWordNet
162
EuroWordNet D006: Definition of the links and subsets for verbs
5.3.2 Motion verb synsets and taxonomy
In the following, the synsets and taxonomy built for motion verbs are reported. For some verbs
(the most interesting from the point of view of the issues raised) we indicate: a) the LDB sense
considered; b) the corresponding synset built on the basis both of data from dictionaries and of personal
intuitions/decisions; the corresponding synset(s) in WordNet 1.5 (indicated as WN).
muoversi
Sense 1
muoversi -- (to move - no specification either of direction or manner)
[WN: Sense 3 of to move]
=> agitarsi 0_1
agitarsi, tempestare (to be agitated) [WN: Sense 1 of to be agitated]
=> dibattersi (to struggle, to flounder)
=> sciaguattare (to move in a container - said of liquids)
=> sguazzare (to wallow)
=> spagliare (to scatter straw)
=> andare 0_1a
andare, muovere (to go along an unbounded path) [WN: ? Sense 1 of to go]
=> accompagnare 0_1a
accompagnare, guidare, portare, andare insieme, scortare (to accompany, to go with)
[WN: Sense 2 of to accompany]
=> aggirarsi (to wander about, to go about)
=> arrancare (to plod along, to trudge)
=> avanzare 0_1b
avanzare, andare avanti, procedere, proseguire (to move forward, to advance into, to
progress) [WN: Sense 1 of to advance]
=> bighellonare (to lounge, to loaf)
=> camminare 0_1
camminare, andare a piedi (to walk, to go on foot) [WN: Sense 1 of to walk]
=> cavalcare 0_0
cavalcare, andare a cavallo (to ride) [WN: Sense 1 of to ride]
=> ciondolare (to dangle)
=> circolare (to circulate, to move round)
=> concorrere (to come together, to assemble)
=> correre 0_1 (to run along an unbounded path) [WN: ?Sense 1 of to run]
=> derivare (to drift)
=> errare 0_1
vagare, vagabondare, peregrinare, girovagare (to wander about, around) [WN: Sense 1
of to wander]
LE2-4003
EuroWordNet
163
EuroWordNet D006: Definition of the links and subsets for verbs
=> foraggiare (to go around the country to look for foraging)
=> galoppare (to gallop)
=> girandolare 0_0
girandolare, girellare, gironzolare, girovagare (to saunter, to stroll) [WN: Sense 1 of to
saunter]
=> girare (0_2a)
girare, percorrere, visitare (to travel, to tour) [WN: ?? Sense 2 and Sense 3 of to travel]
=> girellare 0_0
girellare, girandolare, gironzolare, girovagare (to saunter, to stroll) [WN: Sense 1 of to
saunter]
=> gironzolare 0_0
gironzolare, girandolare, girellare, girovagare (to saunter, to stroll) [WN: Sense 1 of to
saunter]
=> girovagare 0_1
girovagare, girandolare, girellare, gironzolare (to saunter, to stroll) [WN: Sense 1 of to
saunter]
=> navigare 0_0
navigare, viaggiare (to sail, to navigate, to go, to travel - said of ships or airplanes)
[WN: Sense 1 and sense 3 of to navigate]
=> orzare (to luff)
=> pattugliare (to patrol)
=> peregrinare 0_1
peregrinare, errare, vagabondare, vagare, girovagare 0_2 (to wander, to roam) [WN:
Sense 1 of to wander]
=> precedere (to go ahead)
=> procedere 0_1
avanzare, andare avanti, procedere, proseguire (to move forward, to advance into, to
progress) [WN: Sense 1 of to advance]
=> proseguire 0_0
avanzare, andare avanti, procedere, proseguire (to move forward, to advance into, to
progress) [WN: Sense 1 of to advance]
=> scarrozzare (to drive about (in a carriage))
=> seguire (to follow)
=> sgonnellare (to wiggle)
=> slittare (to slide)
=> trottare (to trot)
=> trotterellare (to trot along, to toddle)
=> uccellare (to go fowling)
=> vagabondare 0_1b
vagabondare, girovagare, peregrinare, vagare, errare (to wander (a place), to rove (a
place)) [WN: Sense 1 of to wander]
=> vagare 0_0
LE2-4003
EuroWordNet
164
EuroWordNet D006: Definition of the links and subsets for verbs
vagare, vagabondare, girovagare, peregrinare, errare (to wander (a place), to rove (a
place)) [WN: Sense 1 of to wander]
=> andare 0_1b
andare, recarsi, portarsi, trasferirsi (to go to a place, to change position) [WN: ? Senses 1 and 2
of to go]
=> accompagnare 0_1a
accompagnare, guidare, portare, andare insieme, scortare (to accompany, to go with)
[WN: Sense 2 of to accompany]
=> affondare (to sink)
=> affrontare (to go against someone)
=> allontanarsi 0_0a
allontanarsi, discostarsi, muoversi (0_2), scostarsi (to move/go away) [WN: Senses 1
and 2 of to go away]
=> allontanarsi 0_0b
allontanarsi, andare lontano (to go far) [WN: ???]
=> cadere 0_1
cadere, cascare (to fall down) [WN: Sense 1 of to fall down]
=> cascare 0_0
cascare, cadere (to fall down) [WN: Sense 1 of to fall down]
=> congedarsi (to take one’s leave)
=> coricarsi (to go to bed)
=> correre 0_3
correre, accorrere, affrettarsi (to run along bounded path, to run to a place) [WN: ?
Sense 1 of to run]
=> discendere (to go down)
=> dividersi (to go towards different directions)
=> entrare 0_1b
entrare, andare dentro (to go into) [WN: Sense 1 of to go into]
=> entrare 0_1a
entrare, addentrarsi, inoltrarsi, penetrare (to go into, to penetrate, to get into) [WN:
Sense 2 of to go into]
=> esiliarsi 0_0
esiliarsi, esulare (to go into exile)
=> esulare 0_0
esulare, esiliarsi (to go into exile)
=> naufragare (to be wreck)
=> riandare (to go to a place again)
=> ribaltarsi (to upset)
=> rilevare (to call for)
=> ritornare (to go back again)
=> salire (to go up)
=> scendere (to go down)
=> sloggiare (to clear out)
LE2-4003
EuroWordNet
165
EuroWordNet D006: Definition of the links and subsets for verbs
=> spargersi (to scatter, to spread)
=> spatriare (to emigrate)
=> tornare (to go/come back)
=> uscire (to go out)
=> brulicare (to swarm)
=> dimenarsi (to move about restlessly)
=> dondolare (to swing)
=> formicolare (to swarm)
=> girare (0_1) (to turn) [WN: Sense 1 of to turn]
=> gravitare (to gravitate)
=> guizzare (to wriggle, to frisk)
=> marciare (to march)
=> navigare (to sail, to navigate)
=> nuotare (to swim)
=> ondeggiare (to rock, to wave, to sway, to flutter, to ripple, to flicker, to blow ...) [WN: Sense
2 of to rock; Sense 2 of to flutter; Sense 1 of to ripple ...]
=> ondeggiare, barcollare (to sway, to vacillate) [WN: Sense 2 of to sway; Sense 2 of to
vacillate, which is a hyponym of Sense 2 of to sway ???]
=> oscillare, tentennare, dondolare, ciondolare, vacillare (to swing, to sway, to flicker, to
waver, to oscillate, to rock, to vacillate ...) [WN: Sense 2 of to swing and of to sway (same
synset); the same as for ondeggiare above]
=> passare, transitare (to pass (through a place), to go along) [WN: Sense 3 of to pass]
=> scatenarsi (?? to move with a great energy, or with violence)
=> scorrere (to run, to glide, to slide, to flow) [WN: Sense 7 of to run; Sense 1 of to slide]
=> scrollarsi (to shake)
=> spaziare (to space, to range)
=> sventolare (to wave, to flutter)
=> tramestare (to move in a noisy way)
=> vacillare, pencolare, traballare, tentennare (to vacillate) [WN: cf. above]
=> vibrare (to vibrate, to quiver)
=> volare (to fly) [WN: Sense 1]
5.3.3 Know verb synsets and taxonomies
sapere
Sense 1
sapere, conoscere (to know, to be aware of something) [WN: Sense 1 of to know]
(essere a conoscenza di qlc., avere notizia di qlc.)
Sense 2
LE2-4003
EuroWordNet
166
EuroWordNet D006: Definition of the links and subsets for verbs
sapere, conoscere, avere familiarità con, intendere, capire, comprendere (to know, to have a familiarity
with, to understand) [WN: Sense 2 of to know]
=> padroneggiare (to master)
=> digerire, assimilare (to understand well)
=> penetrare (to penetrate)
=> decodificare
Sense 3
sapere, conoscere, discernere, distinguere, riconoscere, ravvisare (to be aware of, to discern, to
distinguish) [WN: sense 2 of to distinguish]
Sense 4
sapere, conoscere, apprendere, divenire consapevole, capire (to know, to become aware of, to realize)
[WN: Sense 2 of to realize]
=> imparare (to learn)
=> impadronirsi (to begin to master)
=> memorizzare (to memorize)
=> afferrare, intuire (to realize, to understand immediately without reasoning)
Sense 5
sapere, essere in grado, essere capace
Sense 6
sapere, presagire
conoscere
Sense 1
sapere, conoscere (to know, to be aware of something) [WN: Sense 1 of to know]
Sense 2
sapere, conoscere, avere familiarità con, intendere, capire, comprendere (to know, to have a familiarity
with, to understand) [WN: Sense 2 of to know]
=> padroneggiare (to master)
=> digerire, assimilare (to understand well)
=> penetrare (to penetrate)
=> decodificare (to decode)
Sense 3
LE2-4003
EuroWordNet
167
EuroWordNet D006: Definition of the links and subsets for verbs
sapere, conoscere, discernere, distinguere, riconoscere, ravvisare (to be aware of, to discern, to
distinguish) [WN: sense 2 of to distinguish]
Sense 4
sapere, conoscere, apprendere, divenire consapevole, capire (to know, to become aware of, to realize)
[WN: Sense 2 of to realize]
=> imparare (to learn)
=> impadronirsi (to begin to master)
=> memorizzare (to memorize)
=> afferrare, intuire (to realize, to understand immediately without reasoning)
Sense 5
conoscere, avere familiarità con, sapere chi è qualcuno (to know someone, to have a familiarity with
someone) [WN: Sense 4 of to know]
5.4 Data obtained by the analysis performed on Spanish
5.4.1 List of Base Concepts
104 synsets
Structure of the file:
WNsynsetId Lexicographersfile 1stEnglishwordofWNsynset SpanishSynset freqasGenus freqinMRD
freqinCorpus
00021823 29 look {parecer} 14 635 2267
00033668 29 wear {llevar,usar} 273 996 2780
00037593 29 bear {llevar} 192 932 2400
00048767 29 treat {tratar} 122 579 1759
00068499 30 keep_up {seguir} 62 311 1599
00079704 30 leave {dejar} 402 655 2139
00104355 30 remove {quitar,sacar} 873 1167 906
00113224 30 insert {poner,colocar} 1552 2597 3008
00204516 30 happen {pasar} 308 724 2573
00210630 30 go_on {seguir} 62 311 1599
00217088 30 leave {dejar} 402 655 2193
00311709 30 issue {salir} 220 552 1357
LE2-4003
EuroWordNet
168
EuroWordNet D006: Definition of the links and subsets for verbs
00331061 31 understand {ver} 80 230 5185
00333362 31 know {saber,conocer} 47 301 4296
00333754 31 know {saber} 26 119 2868
00334020 31 know {saber} 26 119 2868
00341396 31 remember {recordar} 14 60 1005
00341811 31 think {pensar} 8 56 1598
00342479 31 remember {pensar} 15 56 1598
00343126 31 remind {recordar} 14 60 1005
00343621 31 remember {recordar} 14 60 1005
00343810 31 commemorate {recordar} 14 60 1005
00345074 31 abandon {dejar} 402 655 2193
00354465 31 think {pensar} 8 56 1598
00359802 31 get {obtener,sacar} 407 1098 1129
00366972 31 identify {llamar} 68 383 1659
00405636 31 expect {esperar} 16 76 1153
00416049 31 arrange {poner} 1478 2049 2671
00432532 32 induce {hacer,causar} 3054 6095 9045
00451248 32 permit {dejar} 402 655 2193
00535241 32 consider {contar} 38 139 1069
00551959 32 pronounce {decir} 286 719 7424
00557762 32 address {hablar} 204 760 2097
00611702 33 drop_out {dejar} 402 655 2193
00620218 33 lose {perder} 304 417 1020
00656714 34 consume {tomar} 426 699 1210
00658243 34 use {usar,emplear} 94 2827 683
00661760 34 use {usar,emplear} 94 2827 683
00675686 34 need {querer} 18 119 2499
00691086 35 take {tomar,obtener,sacar} 620 1797 2338
00763269 35 cover {cubrir} 334 937 330
00848136 35 free {quitar} 1002 640 328
00877616 35 function {ir} 148 779 11701
00926188 36 create {crear,causar} 222 685 1812
00926361 36 make {crear,realizar,causar} 264 977 2848
01008772 37 feel {sentir} 130 227 1049
01011032 37 love {querer} 18 119 2499
01040073 37 desire {querer} 18 119 2499
01046072 38 travel {ir} 148 779 11701
01054314 38 go {ir,salir} 294 1331 13058
01141779 38 lead {llevar} 192 932 2400
01144761 38 arrive {llegar} 196 343 2720
01146905 38 stay {quedar} 96 522 1957
01152122 38 enter {entrar} 96 1775 963
01154482 38 reach {llegar} 154 343 2720
LE2-4003
EuroWordNet
169
EuroWordNet D006: Definition of the links and subsets for verbs
01156236 38 meet {encontrar} 14 277 2026
01202814 39 feel {sentir} 130 227 1049
01203891 39 experience {tener} 546 3946 8289
01214474 39 miss {perder} 304 417 1020
01214832 39 watch {mirar} 50 102 1447
01216027 39 look {mirar} 50 102 1447
01226339 39 look_at {mirar} 50 102 1447
01227418 39 watch {mirar} 50 102 1447
01230190 39 produce {producir} 238 1227 1386
01255335 40 give {dar} 1942 2509 5054
01256853 40 have {tener} 546 3946 8289
01257491 04 own {tener} 546 3946 8289
01261345 40 get {tomar,obtener,sacar} 620 1797 2338
01267839 40 get_rid_of {quitar} 1002 640 328
01280035 40 find {encontrar} 14 277 2026
01283168 40 owe {deber} 2 418 2057
01286642 40 trade {tratar} 122 579 1759
01289475 40 account {contar} 38 139 1069
01301401 40 lose {perder} 304 417 1020
01301721 40 lose {perder} 304 417 1020
01302104 40 lose {perder} 304 417 1020
01317872 40 give {dar} 1942 2509 5054
01350197 41 install {poner} 1478 2049 2671
01360902 41 remove {quitar,sacar} 873 1167 906
01361498 41 take_out {quitar,sacar} 873 1167 906
01371393 41 let {dejar} 402 655 2193
01448761 41 effect {hacer} 2854 5522 8818
01469362 41 meet {conocer} 42 182 1428
01471536 42 exist {haber,existir} 54 1711 21521
01472320 42 be {ser} 260 3452 48737
01475351 42 end {acabar} 38 104 1009
01476675 42 be {haber} 40 1588 20355
01477977 42 live {vivir} 62 543 1434
01480699 42 survive {vivir} 62 543 1434
01482115 42 constitute {ser} 260 3452 48737
01492762 42 wait {esperar} 16 76 1153
01501697 42 be {estar} 342 900 5542
01506899 42 equal {ser} 260 3452 48737
01513147 42 refer {tratar} 122 579 1759
01515268 42 discontinue {dejar} 402 655 2193
01531792 42 spend {pasar} 308 724 2573
01535432 42 postdate {seguir} 62 311 1599
01539155 42 can {poder,saber} 33 2184 10619
LE2-4003
EuroWordNet
170
EuroWordNet D006: Definition of the links and subsets for verbs
01539245 42 can {poder} 20 2065 7751
01539308 42 must {deber} 2 418 2057
01539689 42 must {deber} 2 418 2057
01539895 42 should {deber} 2 418 2057
01546360 42 sell {vender} 68 695 278
01548592 42 persist {quedar} 96 522 1957
01556433 42 have {tratar} 122 579 1759
5.4.2 Motion verb synsets and taxonomies
VERBS OF MOVEMENT EXTRACTED SO FAR: taxonomies of 'ir', 'mover' and 'moverse'
4.4.1 IR TAX
ir_1_1
ir_1_1 acudir_1_1
ir_1_1 acudir_1_1 acorer_1_2
ir_1_1 acudir_1_1 afluir_1_1
ir_1_1 acudir_1_2
ir_1_1 andar_2_1
ir_1_1 andar_2_1 acular_1_4
ir_1_1 andar_2_1 agilar_1_1
ir_1_1 andar_2_1 amblar_1_1
ir_1_1 andar_2_1 amblar_1_1 anquear_1_1
ir_1_1 andar_2_1 ambular_1_1
ir_1_1 andar_2_1 anadear_1_1
ir_1_1 andar_2_1 anadear_1_1 nanear_1_1
ir_1_1 andar_2_1 apeonar_1_1
ir_1_1 andar_2_1 atrochar_1_1
ir_1_1 andar_2_1 bandear_2_2
ir_1_1 andar_2_1 bodegonear_1_1
ir_1_1 andar_2_1 cabalgar_1_1
ir_1_1 andar_2_1 cabalgar_1_1 subir_1_2
ir_1_1 andar_2_1 cabalgar_1_1 trotar_1_2
ir_1_1 andar_2_1 cabalear_1_1
ir_1_1 andar_2_1 calejear_1_1
ir_1_1 andar_2_1 calejear_1_1 rutiar_1_1
ir_1_1 andar_2_1 caminar_1_2
ir_1_1 andar_2_1 camotear_1_1
ir_1_1 andar_2_1 cantonear_1_1
ir_1_1 andar_2_1 cazcalear_1_1
LE2-4003
EuroWordNet
171
EuroWordNet D006: Definition of the links and subsets for verbs
ir_1_1 andar_2_1 cazcalear_1_1 andorear_1_1
ir_1_1 andar_2_1 cazcalear_1_1 andorear_1_1 andareguear_1_1
ir_1_1 andar_2_1 cejar_1_1
ir_1_1 andar_2_1 cejar_1_1 acular_1_5
ir_1_1 andar_2_1 cejar_1_1 recular_1_1
ir_1_1 andar_2_1 cejar_1_1 recular_1_1 recejar_1_1
ir_1_1 andar_2_1 cejar_1_1 recular_1_1 retrechar_1_1
ir_1_1 andar_2_1 circular_2_1
ir_1_1 andar_2_1 circular_2_1 corer_1_9
ir_1_1 andar_2_1 cojear_1_1
ir_1_1 andar_2_1 cojear_1_1 chenquear_1_1
ir_1_1 andar_2_1 coretear_1_1
ir_1_1 andar_2_1 coretear_1_1 hopear_1_2
ir_1_1 andar_2_1 culebrear_1_1
ir_1_1 andar_2_1 danzar_1_3
ir_1_1 andar_2_1 deambular_1_1
ir_1_1 andar_2_1 deambular_1_1 divagar_1_1
ir_1_1 andar_2_1 deambular_1_1 divagar_1_1 erar_1_4
ir_1_1 andar_2_1 deambular_1_1 divagar_1_1 trafagar_1_3
ir_1_1 andar_2_1 deambular_1_1 rular_1_3
ir_1_1 andar_2_1 discurir_1_1
ir_1_1 andar_2_1 discurir_1_1 pasear_1_6
ir_1_1 andar_2_1 discurir_1_1 travesear_1_2
ir_1_1 andar_2_1 erar_1_3
ir_1_1 andar_2_1 escarabajear_1_1
ir_1_1 andar_2_1 ganadear_1_1
ir_1_1 andar_2_1 gatear_1_2
ir_1_1 andar_2_1 gatear_1_5
ir_1_1 andar_2_1 jinetear_1_1
ir_1_1 andar_2_1 ladear_1_3
ir_1_1 andar_2_1 lanear_1_1
ir_1_1 andar_2_1 marchar_1_1
ir_1_1 andar_2_1 marchar_1_1 andonear_1_1
ir_1_1 andar_2_1 marchar_1_1 desfilar_1_1
ir_1_1 andar_2_1 marchar_1_1 desfilar_1_3
ir_1_1 andar_2_1 marchar_1_1 rodar_1_9
ir_1_1 andar_2_1 montear_1_5
ir_1_1 andar_2_1 nagualear_1_1
ir_1_1 andar_2_1 navegar_1_2
ir_1_1 andar_2_1 navegar_1_3
ir_1_1 andar_2_1 navegar_1_4
ir_1_1 andar_2_1 pasear_1_1
ir_1_1 andar_2_1 pasear_1_1 carocear_1_1
LE2-4003
EuroWordNet
172
EuroWordNet D006: Definition of the links and subsets for verbs
ir_1_1 andar_2_1 pasear_1_1 rondar_1_4
ir_1_1 andar_2_1 pasear_1_1 ruar_1_2
ir_1_1 andar_2_1 pasear_1_3
ir_1_1 andar_2_1 pasear_1_7
ir_1_1 andar_2_1 patear_1_4
ir_1_1 andar_2_1 patojear_1_1
ir_1_1 andar_2_1 peregrinar_1_1
ir_1_1 andar_2_1 peregrinar_1_4
ir_1_1 andar_2_1 pernear_1_2
ir_1_1 andar_2_1 recorer_1_1
ir_1_1 andar_2_1 recorer_1_1 bajar_1_5
ir_1_1 andar_2_1 recorer_1_1 caminar_1_4
ir_1_1 andar_2_1 recorer_1_1 campear_1_6
ir_1_1 andar_2_1 recorer_1_1 corer_1_13
ir_1_1 andar_2_1 recorer_1_1 corer_1_24
ir_1_1 andar_2_1 recorer_1_1 pampear_1_1
ir_1_1 andar_2_1 recorer_1_1 patear_1_5
ir_1_1 andar_2_1 recorer_1_1 peregrinar_1_5
ir_1_1 andar_2_1 recorer_1_1 rodar_1_6
ir_1_1 andar_2_1 recorer_1_1 subir_1_10
ir_1_1 andar_2_1 recorer_1_1 viajar_1_5
ir_1_1 andar_2_1 reptar_1_1
ir_1_1 andar_2_1 rodear_1_1
ir_1_1 andar_2_1 rodear_1_1 arodear_1_1
ir_1_1 andar_2_1 rondar_1_2
ir_1_1 andar_2_1 rondar_1_3
ir_1_1 andar_2_1 rondar_1_6
ir_1_1 andar_2_1 ruar_1_1
ir_1_1 andar_2_1 rumbear_1_2
ir_1_1 andar_2_1 talonear_1_1
ir_1_1 andar_2_1 terear_1_2
ir_1_1 andar_2_1 tesar_1_2
ir_1_1 andar_2_1 trafagar_1_1
ir_1_1 andar_2_1 trafagar_1_4
ir_1_1 andar_2_1 trajinar_1_2
ir_1_1 andar_2_1 travesear_1_1
ir_1_1 andar_2_1 trotar_1_3
ir_1_1 andar_2_1 tumbear_1_1
ir_1_1 andar_2_1 tunar_1_1
ir_1_1 andar_2_1 tunar_1_1 lampar_1_2
ir_1_1 andar_2_1 vagabundear_1_1
ir_1_1 andar_2_1 vagabundear_1_1 bundear_1_2
ir_1_1 andar_2_1 vagabundear_1_1 vagamundear_1_1
LE2-4003
EuroWordNet
173
EuroWordNet D006: Definition of the links and subsets for verbs
ir_1_1 andar_2_1 vagar_3_1
ir_1_1 andar_2_1 vagar_3_2
ir_1_1 andar_2_1 vagar_3_3
ir_1_1 andar_2_1 ventear_1_12
ir_1_1 andar_2_1 ventear_1_4
ir_1_1 andar_2_1 voltear_1_8
ir_1_1 andar_2_1 zancajear_1_1
ir_1_1 andar_2_1 zancajear_1_1 zanquear_1_2
ir_1_1 andar_2_1 zanganear_1_1
ir_1_1 andar_2_1 zarcear_1_3
ir_1_1 andar_2_1 zigzaguear_1_1
ir_1_1 aribar_1_3
ir_1_1 aribar_1_5
ir_1_1 atrechar_1_1
ir_1_1 barquear_1_1
ir_1_1 bordear_1_1
ir_1_1 bordear_1_1 bordejar_1_1
ir_1_1 bordonear_1_1
ir_1_1 cabalgar_1_3
ir_1_1 caminar_1_1
ir_1_1 caminar_1_1 arear_1_5
ir_1_1 caminar_1_1 chuequear_1_1
ir_1_1 caminar_1_1 corer_1_1
ir_1_1 caminar_1_1 corer_1_1 acosar_1_3
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1 ascender_1_2
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1 avanzar_1_4
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1 avanzar_1_6
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1 corer_1_17
ir_1_1 caminar_1_1 corer_1_1 adelantar_1_1 salir_1_35
ir_1_1 caminar_1_1 corer_1_1 carerear_1_2
ir_1_1 caminar_1_1 corer_1_1 circular_2_2
ir_1_1 caminar_1_1 corer_1_1 coretear_1_2
ir_1_1 caminar_1_1 corer_1_1 descender_1_2
ir_1_1 caminar_1_1 corer_1_1 descorer_1_3
ir_1_1 caminar_1_1 corer_1_1 desempedrar_1_2
ir_1_1 caminar_1_1 corer_1_1 despatilar_1_5
ir_1_1 caminar_1_1 corer_1_1 escapar_1_7
ir_1_1 caminar_1_1 corer_1_1 fluir_1_1 afluir_1_3
ir_1_1 caminar_1_1 corer_1_1 fluir_1_1 discurir_1_2
ir_1_1 caminar_1_1 corer_1_1 fluir_1_1 efluir_1_1
ir_1_1 caminar_1_1 corer_1_1 gambetear_1_2
ir_1_1 caminar_1_1 corer_1_1 lanzar_1_6
LE2-4003
EuroWordNet
174
EuroWordNet D006: Definition of the links and subsets for verbs
ir_1_1 caminar_1_1 corer_1_1 lanear_1_2
ir_1_1 caminar_1_1 corer_1_1 mundear_1_1
ir_1_1 caminar_1_1 cruzar_1_13
ir_1_1 caminar_1_1 despezuÒarse_1_2
ir_1_1 caminar_1_1 faldear_1_1
ir_1_1 caminar_1_1 faldear_1_1 enfaldar_1_3
ir_1_1 caminar_1_1 ir_1_5
ir_1_1 caminar_1_1 marchar_1_4
ir_1_1 caminar_1_1 samurear_1_1
ir_1_1 caminar_1_1 surcar_1_3
ir_1_1 caminar_1_1 tanguear_1_1
ir_1_1 caminar_1_1 venir_1_1
ir_1_1 caminar_1_1 venir_1_1 caer_1_1
ir_1_1 caminar_1_1 venir_1_1 caer_1_1 aterizar_1_3
ir_1_1 caminar_1_1 venir_1_1 caer_1_1 deslizar_1_5
ir_1_1 caminar_1_1 venir_1_1 caer_1_1 rodar_1_14
ir_1_1 caminar_1_1 venir_1_1 caer_1_1 rodar_1_3
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1 acometer_1_1
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1 acometer_1_1 entrar_1_10
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1 acometer_1_1 entrar_1_6
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1 acometer_1_1 partir_1_6
ir_1_1 caminar_1_1 venir_1_1 embestir_1_1 avanzar_1_2
ir_1_1 caminar_1_1 venir_1_1 revolotear_1_2
ir_1_1 caminar_1_1 venir_1_1 salir_1_21
ir_1_1 caminar_1_1 volar_1_5
ir_1_1 corer_1_34
ir_1_1 destorentarse_1_2
ir_1_1 galopar_1_1
ir_1_1 galopar_1_1 galopear_1_1
ir_1_1 galopar_1_1 galuchar_1_1
ir_1_1 legar_1_10
ir_1_1 pasar_1_23
ir_1_1 peregrinar_1_2
ir_1_1 proceder_2_1
ir_1_1 proceder_2_1 descender_1_4
ir_1_1 proceder_2_1 salir_1_19
ir_1_1 proceder_2_1 tunear_1_2
ir_1_1 rastrear_1_6
ir_1_1 rodear_1_2
ir_1_1 rolar_1_2
ir_1_1 seguir_1_1
ir_1_1 seguir_1_1 cabestrear_1_1
LE2-4003
EuroWordNet
175
EuroWordNet D006: Definition of the links and subsets for verbs
ir_1_1 seguir_1_1 cabestrear_1_1 ramalear_1_1
ir_1_1 seguir_1_1 cabestrear_1_4
ir_1_1 seguir_1_1 caminar_1_3
ir_1_1 seguir_1_1 curvear_1_1
ir_1_1 seguir_1_1 ir_1_3
ir_1_1 seguir_1_1 pasar_1_9
ir_1_1 seguir_1_1 perseguir_1_1
ir_1_1 seguir_1_1 perseguir_1_1 acosar_1_1
ir_1_1 seguir_1_1 perseguir_1_1 acosar_1_1 corer_1_18
ir_1_1 seguir_1_1 perseguir_1_1 acosar_1_1 seguir_1_4
ir_1_1 seguir_1_1 perseguir_1_1 acosar_1_1 sitiar_1_2
ir_1_1 seguir_1_1 perseguir_1_1 arinconar_1_2
ir_1_1 seguir_1_1 perseguir_1_1 asenderear_1_2
ir_1_1 seguir_1_1 perseguir_1_1 bandear_2_3
ir_1_1 seguir_1_1 perseguir_1_1 carabritear_1_1
ir_1_1 seguir_1_1 perseguir_1_1 colear_1_8
ir_1_1 seguir_1_1 perseguir_1_1 encorer_1_1
ir_1_1 seguir_1_1 perseguir_1_1 sabanear_1_4
ir_1_1 seguir_1_1 rumbar_1_4
ir_1_1 seguir_1_1 siguetear_1_1
ir_1_1 seguir_1_3
ir_1_1 seguir_1_7
ir_1_1 subir_1_8
ir_1_1 trotar_1_1
ir_1_1 trotar_1_1 trochar_1_1
ir_1_1 trotar_1_1 trotear_1_1
ir_1_1 trotar_1_1 trotinar_1_1
ir_1_1 volar_1_4
MOVER TAX
mover I (1)
aballar I (1)
abrir I (7)
adelantar I (1)
agitar I (1)
chapotear I (3)
guachapear I (1)
menear I (1)
hopear I (1)
hurgar I (1)
hurgonear I (1)
LE2-4003
EuroWordNet
176
EuroWordNet D006: Definition of the links and subsets for verbs
rabear I (1)
revolver I (1)
agitar I (3)
arrebujar I (3)
batir I (6)
mazar I (1)
merengar I (1)
trabajar I (5)
buscar I (2)
cacear I (1)
enrehojar I (1)
escaramuzar I (2)
papelear I (1)
trastear II (1)
tabalear I (1)
zalear I (1)
revolver I (1)
silbar I (2)
tabalear I (1)
traquetear I (2)
ventilar I (2)
vibrar I (5)
alear I (1)
aletear I (1)
aletear I (2)
apalancar I (1)
bailar I (1)
bailotear I (1)
danzar I (1)
polcar I (1)
tripudiar I (1)
valsar I (1)
valsar I (1)
bandear I (1)
batir I (5)
batir I (6)
befar I (1)
blandir I (1)
blandear II (1)
doblegar I (2)
bocezar I (1)
bocear I (1)
bornear I (3)
LE2-4003
EuroWordNet
177
EuroWordNet D006: Definition of the links and subsets for verbs
bracear I (1)
cambiar I (5)
contrabracear I (1)
cabalgar I (2)
cabecear I (1)
cavar I (1)
agostar I (2)
agostar I (3)
azadonar I (1)
entrecavar I (1)
escavanar I (1)
escavar I (1)
escopetar I (1)
hornaguear I (1)
jirpear I (1)
rebinar I (1)
cerner I (7)
codear I (1)
colear I (1)
contonearse I (1)
campanear I (4)
remenearse I (1)
zarandear I (3)
florear I (3)
goncear I (1)
ijadear I (1)
levantar I (1)
alfar II (1)
alzar I (1)
elevar I (1)
bombear I (3)
remontar I (2)
piafar I (1)
atabalear I (1)
sobrealzar I (1)
sofaldar I (1)
apalancar I (1)
arrezagar I (2)
aupar I (1)
upar I (1)
cavar I (1)
desvenar I (4)
empinar I (1)
LE2-4003
EuroWordNet
178
EuroWordNet D006: Definition of the links and subsets for verbs
empingorotar I (1)
enarbolar I (1)
arbolar I (1)
tremolar I (1)
encampanar I (3)
encaramar I (1)
engarriar I (1)
encumbrar I (1)
engallarse I (1)
enhestar I (1)
erguir I (1)
escalar I (4)
hozar I (1)
hocicar I (1)
incorporar I (2)
realzar I (1)
rebotar I (5)
solevantar I (1)
solevar I (2)
sopesar I (1)
sompesar I (1)
sospesar I (1)
suspender I (1)
lomear I (1)
menear I (1)
motorizar I (1)
mecanizar I (1)
nalguear I (1)
orejear I (1)
palear I (2)
patalear I (1)
pernear I (1)
pestaÒear I (1)
rabear I (2)
remar I (1)
bogar I (1)
bogar I (2)
ciar I (1)
paletear I (1)
proejar I (1)
singar I (1)
remecer I (1)
remolinear I (1)
LE2-4003
EuroWordNet
179
EuroWordNet D006: Definition of the links and subsets for verbs
rizar I (3)
ronzar II (1)
arronzar I (1)
sacudir I (1)
desgargolar I (1)
despolvorear I (1)
espolvorear I (1)
escodar II (1)
zamarrear I (1)
teclear I (1)
mover I (3 4 5)
compungir I (1)
conmover I (1)
decidir I (2)
despertar I (4)
enternecer I (2)
incitar I (1)
inducir I (1)
ocasionar I (2)
quebrantar I (8)
mover I (7)
mover I (8)
mover I (9)
MOVERSE TAX
moverse I (1)
agarrotar I (5)
andar II (1)
abordonar I (1)
acuartillar I (2)
amblar I (1)
anadear I (1)
nanear I (1)
apeonar I (1)
atrochar I (1)
barbear I (4)
caminar I (2)
arrear I (5)
cruzar I (13)
faldear I (1)
ladear I (3)
venir I (1)
LE2-4003
EuroWordNet
180
EuroWordNet D006: Definition of the links and subsets for verbs
advenir I (1)
llegar I (1)
revolotear I (2)
sobrevenir I (2)
cejar I (1)
garrar I (1)
garrear I (1)
recular I (1)
recejar I (1)
retrechar I (1)
cerner I (6)
chancletear I (1)
circular II (1)
roldar I (1)
cochear I (3)
cojear I (1)
culebrear I (1)
danzar I (3)
deambular I (1)
rular I (3)
discurrir I (1)
errar I (3)
escarabajear I (1)
gatear I (2)
jinetear I (1)
juntar I (5)
ladear I (3)
llanear I (1)
lustrar I (3)
navegar I (1)
navegar I (3)
noctambular I (1)
parrandear I (1)
pasear I (1)
deambular I (1)
ruar I (2)
pasear I (3)
pecorear I (3)
peregrinar I (1)
lustrar I (3)
raquear I (1)
ratear III (1)
renquear I (1)
LE2-4003
EuroWordNet
181
EuroWordNet D006: Definition of the links and subsets for verbs
reptar I (1)
retrasar I (3)
desacelerar I (1)
rodear I (1)
abarcar I (5)
cercar I (1)
cercar I (3)
circuir I (1)
circundar I (1)
circunvalar I (1)
rondar I (2)
rondar I (3)
roldar I (1)
talonear I (1)
tesar I (2)
trafagar I (1)
trafagar I (4)
trajinar I (2)
travesear I (1)
vagar III (1)
flanear I (1)
vaguear I (1)
vagar III (3)
zancajear I (1)
zigzaguear I (1)
andar II (3)
marchar I (1)
navegar I (2)
bailar I (3)
escarabajear I (5)
bailar I (6)
bambolear I (1)
bambalear I (1)
bambanear I (1)
bambonear I (1)
blandir I (2)
bullir I (3)
bullir I (5)
cabecear I (3)
circular II (1)
danzar I (2)
girar I (1)
arribar I (6)
LE2-4003
EuroWordNet
182
EuroWordNet D006: Definition of the links and subsets for verbs
bailar I (5)
bornear I (5)
campanear I (2)
orbitar I (1)
hervir I (1)
borbotar I (1)
borboritar I (1)
brollar I (1)
bullir I (1)
hojear I (3)
hornaguear I (2)
mandar I (8)
manejar I (5)
marchar I (1)
menear I (4)
mimbrear I (1)
ondular I (1)
ondear I (3)
undular I (1)
oscilar I (1)
bascular I (2)
titubear I (1)
tantear I (7)
titubar I (1)
palpitar I (2)
rehilar I (2)
relingar I (3)
revolver I (14)
serpentear I (1)
serpear I (1)
tartalear I (1)
vacilar I (1)
fluctuar I (1)
trastrabillar I (2)
venir I (1)
vibrar I (1)
florear I (3)
volar I (1)
circunvolar I (1)
planear I (4)
remontar I (12)
revolear I (1)
revolotear I (1)
LE2-4003
EuroWordNet
183
EuroWordNet D006: Definition of the links and subsets for verbs
revolar I (2)
abejear I (1)
voltijear I (1)
sobrevolar I (1)
zangolotear I (3)
flanear I (1)
zapatear I (7)
zigzaguear I (1)
5.4.3 Know verb synsets and taxonomies
VERBS OF KNOWING EXTRACTED SO FAR: taxonomies of 'conocer' and 'saber'
CONOCER TAX
adivinar_1_4
conocer_1_1
desconocer_1_3
determinar_1_4
discernir_1_1
distinguir_1_1
dominar_1_3
individualizar_1_1
individuar_1_1
individuar_1_2
palpar_1_3
prevenir_1_2
pronosticar_1_1
saber_2_9
separar_1_4
singularizar_1_1
subdistinguir_1_1
tocar_1_8
SABER TAX
saber_2_1
ignorar_1_1
resaber_1_1
LE2-4003
EuroWordNet
184
EuroWordNet D006: Definition of the links and subsets for verbs
References
Alonge, A. (1992a) ‘Machine-Readable Dictionaries and Lexical Information on Verbs’, in
Proceedings of the 5th Euralex International Congress, Tampere, Finland
Alonge, A. (1992b) Acquisizione di informazioni semantico-sintattiche relative a verbi: analisi di
dizionari di macchina, Tesi di Dottorato, Università di Pisa.
Bloksma, L., P. L. Díez-Orzas, P. Vossen (1996) User Requirements and Functional Specification of
the EuroWordNet Project, Deliverable D001, EuroWordNet, LE2-4003.
Climent, S., H. Rodriguez, J. Gonzalo (1996) Definition of the Links and Subsets for Nouns of the
EuroWordNet Project, Deliverable D005, EuroWordNet, LE2-4003.
Cruse (1986) Lexical Semantics, Cambridge, Cambridge University Press.
Díez-Orzas, P. L., P. Forest, M. Louw (1996) High-Level Architecture of the EuroWordNet Database.
A Novell ConceptNet-Based Semantic Network, Deliverable D007, EuroWordNet, LE2-4003.
Fellbaum (1993) ‘English Verbs as a Semantic Net’, ms.
Gruber, J. (1976)Lexical Structures in Syntax and Semantics, Amsterdam, North-Holland.
Haas, W. (1964) ‘Semantic Value’, in Proceedings of the IXth International Congress of Linguists,,
The Hague, Mouton.
Levin, B. (1993) English verb classes and alternations: a preliminary investigation, Chicago, The
University of Chicago Press.
Lyons, J. (1968) Introduction to Theoretical Linguistics, Cambridge, Cambridge University Press.
Lyons, J. (1977) Semantics, London, Cambridge University Press.
Miller, G, R. Beckwith, C. Fellbaum, D. Gross and K. Miller Five papers on WordNet, CSL Report 43.
Cognitive Science Laboratory. Princeton University. 1990.
Miller, G., R. Beckwith, C. Fellbaum, D Gross, K. Miller (1993) ‘Introduction to WordNet: An
On-Line Lexical Database’, ms.
Montemagni S., (1995) Subject and Object in Italian Sentence Processing, PhD Dissertation, Umist
Manchester, UK.
LE2-4003
EuroWordNet
185
EuroWordNet D006: Definition of the links and subsets for verbs
Sanfilippo, A., T. Briscoe, A. Copestake, M. A. Martì Antonin, A. Alonge
(1992) ‘Translation Equivalence and Lexicalization in the ACQUILEX LKB’, in Proceedings of the
4th International Conference on Theoretical and Methodological Issues in Machine Translation,
Montreal, Canada.
Talmy, L. (1985) ‘Lexicalization Patterns: Semantic Structure in Lexical Form’, in Shopen, T. (cur.),
Language Typology and Syntactic Description: Grammatical Categories and the Lexicon, Cambridge,
Cambridge University Press.
Wright, G. H. von (1963) Norm and Action, New York, Humanities Press.
LE2-4003
EuroWordNet