Download Customizable Modular Lexicalized Parsing

The CHAOS Project:
Theory and Practice
Fabio Massimo Zanzotto
Department of Computer Science, Systems and Production
University of Roma “Tor Vergata”
People

INVESTIGATORS
 Roberto Basili
 Fabio Massimo Zanzotto
 Maria Teresa Pazienza

FORMER CONTRIBUTORS





Daniele Pighin
Daniele Previtali
Alessandro Bahgat
Marco Pennacchiotti
Massimo Di Nanni

Michele Vindigni

Luigi Mazzucchelli

Paola Velardi

Paolo Zirilli

Alessandro Cucchiarelli

Alessandro Marziali

Fabrizio Grisoli

Gianluca De Rossi
Outline
 Theory: Customizable parsing architectures
 XDG: eXtended Dependency Graph
 Task oriented parsing design
 Practice: System Implementation and Use
 A component-based approach
 An object-oriented platform
 Linguistic data
 Processing modules
 How to use the parser in an application
 Demo!!!
Theory
Customizable parsing architectures
Motivation
 The Chaos Project unofficially began in ’96
 … on the long tradition of ARIOSTO (Basili, Pazienza, Velardi) @ the
University of Rome “Tor Vergata” (RTV)
 Aim
 building robust parsers for Italian and for English
 that use verb sub-categorization (syntactic) lexicons induced from
corpora
 that can be used in applications
 Constraints
 use the long tradition @ RTV
 “Social” background




Microtheories for microphenomena
Language analysis can be reduced to a cascade of modules (e.g., FSA)
Application-oriented language anaysis (e.g., IE)
Robust (formely, shallow) parsing approaches
Motivation
contribute-NP-PP(to)
value-NP-PP(at)
Inf(S1)
Inf(S2)
[ Mr. Gaubert ] [contributed] [real estate] [valued] [ at $ 25 million] [to the assets] [of Independent American]
Motivation
(found on vinyl supports)
 Different NLP applications have different
performance constraints in term of:
 Accuracy
 Throughput
 Customizable parsing architectures are reusable
in different application scenarios if:
the architectural design supports performance
control
Customizable parsing
architectures
(found on vinyl supports)
Modularization
 clarifies the interdependency between different
syntactic information (grammatical/lexicalized)
 allows to control
 throughput via eliciting modules
 quality via a clear relation between modules
(prerequisites/contributions)
Modular approach
 Syntactic parser
SP(S,K)=I  SP(S)=I
 Syntactic parsing module:
Pi(Si,Ki)=Si+1  Pi(Si)=Si+1
 Modular syntactic parser
SP = Pn... P2P1
Modular approach
 To push a modular approach we need:
 a suitable annotation scheme
 a classification of the processing modules
A suitable annotation
scheme
 Requirements:
 Modularization
 a stable representation of partially analyzed
structures
 Lexicalization
 a clear representation of the (semantic) head of a
given structure able to activate the lexicalized rule
XDG:
Extended Dependency Graph
 XDG combines constituency and
dependency based formalisms
XDGGD=(C,D)
C = {(c,t,h)|cS,tG,hc}
D = {(c1,c2,t)| c1,c2C, tD}
 Nice property: allow to store persistent
ambiguity (for interpretations projected by
the same nodes)
XDG:
Extended Dependency Graph

C are constituents
 syntactic head
 potential
semantic
governor

D are
dependencies
among
constituents
Classification of parsing
modules
Pi(XDGi,Ki)=Pi(XDGi)=XDGi+1
 The classification is performed according
to:
 the type of information K used
 how they manipulate the sentence
representation
Task oriented parsing
design
 Given:
 The NLP application requirements R
 The test-bed T
 A pool of parsing modules PM
 The designing activity is:
 The research of a combination of the parsing
modules PM that fits R on the T
NLP application
requirements
 Target phenomena: es. VP_PP, NP_PP,
etc
 Metrics:
 Recall R per sentence
 Precision P per sentence
 F-measure per sentence
CHAOS: Levels of Analysis
Dependencies
Clauses
Chunks
NPK
VPK
POS
NNS TO VB IN
PPK
NNS
NPK
VPK
PRP MD
VB
Strategies to use with questions you cannot answer
Verb dependencies and
Clause Boundaries
contribute-NP-PP(to)
value-NP-PP(at)
Inf(S1)
Inf(S2)
[ Mr. Gaubert ] [contributed] [real estate] [valued] [ at $ 25 million] [to the assets] [of Independent American]
Verb dependencies and
Clause Boundaries
contribute-NP-PP(to)
value-NP-PP(at)
Inf(S1)
Inf(S2)
[ Mr. Gaubert ] [contributed] [real estate] [valued] [ at $ 25 million] [to the assets] [of Independent American]
Verb dependencies and
Clause Boundaries
contribute-NP-PP(to)
value-NP-PP(at)
Inf(S1)
Inf(S2)
[ Mr. Gaubert ] [contributed] [real estate] [valued] [ at $ 25 million] [to the assets] [of Independent American]
Verb dependencies and
Clause Boundaries
 The algorithm:
 Initial Hypoteses:
 Minimal boundaries of the clauses in the sentence
 Derived Hierarchy
 Until all verbs have not been analyzed:
 Take the rightmost not analyzed verb v:
 Take the lexicalized rules R(v) for the verb v
 Find the dependencies of
 Augment the clause boundaries
Practice
System Implementation and Use
A Computational
Framework
 Object-oriented backbone
 Objects for the different data
 Objects for the different sub-processes
 Linguistic sub-processors as libraries
 Coexisting languages: Java, C++, C, Prolog
System implementation
 A component-based approach
 An object-oriented platform
 Linguistic data
 Textual entities: Text, Paragraphs
 XDG
 Linguistic processors
A Component-based Approach
Advantages:
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Computational efficiency
Rapid prototyping
Integration of different technologies
Easy reuse
Linguistic processors
Linguistic processors
 Tokenizer, Complex Tokenizer
 Dictionary lookup modules
 Yellow page look-up
 Morphology analyzer


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Name Entity Recognition
Part-of-speech tagging
Chunker
Verb shallow analyzer
Shallow analyzer
Linguistic modules
 Each process is encapsulated in an object

initialize()
 Load lexicons and rules (general or domain specific)

finalize()
 Dismiss the process rules and lexicons

run()
 Enrich the input with the contributes of the process
Linguistic processors
Microtheories for microphenomena
 Each processor implements its own theory:
 It has its language for describing rules
 It is written in its own programming language
Processor:
Yellow page look-up, Morphology analyzer
Dictionary
compra comprare d(a) v.tran.sempl 2.sing.imper.pres ~:u:~
compra comprare d(a) v.tran.sempl 3.sing.ind.pres ~:u:~
comprai comprare d(a) v.tran.sempl 1.sing.ind.pass_rem ~:u:~
comprammo comprare d(a) v.tran.sempl 1.plur.ind.pass_rem ~:u:~
compran comprare d(a) v.tran.sempl 3.plur.ind.pres ~:u:~
comprando comprare d(a) v.tran.sempl geru.pres ~:u:~
comprano comprare d(a) v.tran.sempl 3.plur.ind.pres ~:u:~
Processor:
Chunker
Rules
…
constituent_class([_cst1, _cst2, _cst3], 'VerFin', _mor, 1, 3):verb_finite(_cst1),
verb_to_have(_cst1),
verb_past_particle(_cst2),
verb_to_be(_cst2),
verb_past_particle(_cst3),
common_morfology(_cst1,_mor).
…
Processor:
Verb Shallow Analyser
Sub-categorization lexicon
…
pattern(comprare,[
[(oggetto,Post),(per,Post)],
[(oggetto,Post),(da,Post),(per,Post)],
[(oggetto,Post),(a,Post),(per,Post)],[(oggetto,Post)]]).
pattern(comprendere,[[(oggetto,Post)],[],[(oggetto,Post)]]).
pattern(comprimere,[[(oggetto,Post)],[(oggetto,Post)]]).
pattern(compromettere,[[(con,Post)],[(oggetto,Post)]]).
pattern(comunicare,[[],
[(con,Post)],
[(a,Post)],
[(oggetto,Post),(a,Post)],[(oggetto,Post)]]).
…
Implemented Italian
Shallow Grammar
 Constituent Categories
 Part-of-Speech Tags
 Chunk Types
 Dependency Categories
 Dependency Categories over Chunk Types
A survival user guide
 Version stand-alone:
 chaosparser -h
 Version client-server:
 chaosserver –h
 chaosclient –h
 XDG editor and actual gui:
 choasgui
Using CHAOS in
applications
 In JAVA applications:
ConfigurationHandler.initialize();
ConfigurationHandler.parseKBPropFile(“LANGUAGE”,”KB”);
Parser ms = new Parser();
ms.initialize();
 In Non-JAVA applications:
 Using one of the possible output forms:
 XDG in Xml
 XDG in Prolog
 XDG in QLF (in prolog)
Perspective
 Building a statistical Italian parser
 Increasing the Itailan annotated corpora
 Reusing existing corpora
 TUT
 SITAL
 VIT
Tools
 XDG editor
 DEMO!!!!
 Syntactic annotation transformer
People

INVESTIGATORS
 Roberto Basili
 Fabio Massimo Zanzotto
 Maria Teresa Pazienza

FORMER CONTRIBUTORS





Daniele Pighin
Daniele Previtali
Alessandro Bahgat
Marco Pennacchiotti
Massimo Di Nanni

Michele Vindigni

Luigi Mazzucchelli

Paola Velardi

Paolo Zirilli

Alessandro Cucchiarelli

Alessandro Marziali

Fabrizio Grisoli

Gianluca De Rossi