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Teasing apart syntactic category vs. argument structure information
Teasing apart syntactic category vs. argument structure information

... Bauer (2001: 126) makes a further distinction between strong and weak constraints. A strong constraint describes a process in which an affix attaches only to a particular type of base, such as the suffix -ness in English, which attaches only to adjectives (e.g. happi-ness, white-ness). Strong constr ...
Document
Document

... A change of modality is indicates by a change of auxiliary, the subject remaining the same. ...
CONTENT Introduction: __ _______3 Main part: __ ______14
CONTENT Introduction: __ _______3 Main part: __ ______14

... The actuality of the research Language has four skills: listening, speaking, reading and writing. The degree of one`s proficiency in these skills determines ones achievement in education. Language is shared and structured. It is meaningful and conventional, it is dynamic and systematic, it is comple ...
Using Unknown Word Techniques to Learn Known Words
Using Unknown Word Techniques to Learn Known Words

... we discussed in Section 1. Row (i) contains 4 separate features derived from the prefix of the word and 4 other suffix features are given in row (ii). The two features in rows (iii) and (iv) indicate whether the word starts with a particle and if it contains a hyphen, respectively. Further, the meth ...
Jumping NLP Curves: A Review of Natural Language Processing
Jumping NLP Curves: A Review of Natural Language Processing

... Although the semantic problems and needs of NLP were clear from the very beginning, the strategy adopted by the research community was to tackle syntax first, for the more direct applicability of machine learning techniques. However, there were some researchers who concentrated on semantics because ...
parsing with a small dictionary for applications such as text to speech
parsing with a small dictionary for applications such as text to speech

... literature (Dewar 1969, Bachenko 1986). To the author's knowledge, these latter systems are the only other ones that have attempted parsing on arbitrary text with dictionaries of fewer than 10,000 words. Because the parser described here has access only to a very small dictionary, it cannot exploit ...
Method and device for parsing natural language sentences and
Method and device for parsing natural language sentences and

... have appeared since the original description. The second problem in syntactic parsing has been that Words in natural languages often have multiple meanings; yet, the parsing strategies described above require that the ...
BBI3212 SYNTAX AND MORPHOLOGY
BBI3212 SYNTAX AND MORPHOLOGY

... The work is unfinished. ...
An Accurate Arabic Root-Based Lemmatizer for Information
An Accurate Arabic Root-Based Lemmatizer for Information

... with their present tense forms, because they retain some affixes and internal differences. On the other hand, statistical supervised learning approaches present the best published accuracy as POS taggers. The cost of expanding language coverage is a major problem in supervised learning approaches. I ...
unl deconverter for tamil
unl deconverter for tamil

... between two of the UWs present in the sentence. Nodes, or Universal Words (UWs) are words based on English and disambiguated by their positioning in a knowledge base (KB) of conceptual hierarchies [1]. Function words, such as determiners and auxiliaries are represented in the form of attributes to U ...
Answering Subcognitive Turing Test Questions: A Reply
Answering Subcognitive Turing Test Questions: A Reply

... given a problem word and a set of alternative words, choose the member from the set of alternative words that is most similar in meaning to the problem word. PMI-IR has been evaluated using 80 synonym recognition questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym recogni ...
Answering Subcognitive Turing Test Questions: A
Answering Subcognitive Turing Test Questions: A

... given a problem word and a set of alternative words, choose the member from the set of alternative words that is most similar in meaning to the problem word. PMI-IR has been evaluated using 80 synonym recognition questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym recogni ...
1 What is semantics about? 1.1 Semantics: study of the relation
1 What is semantics about? 1.1 Semantics: study of the relation

... acknowledged, or othewise believed by the language users that the word CHAIR stands for this particular object in the real world and also for all the chairs that there were, are and will exist in the real world. In other words, the word CHAIR, which a physical thing—a sound or a scribble—’stands for ...
Key for Punctuation Practice Test 1. E
Key for Punctuation Practice Test 1. E

... 17. E - There is a comma after various forms of the word "said," but it is not needed after "that." Example: The President said that he "had no idea." Example: Julie always says, "there's no reason for that kind of vulgar behavior." 18. E - This is an odd question at first glance because it appears ...
Stiahnuť prednášku
Stiahnuť prednášku

... which it denotes. However, this dotted line means that there is NO direct connection between the symbol and the thing - instead, it is solved by a mental concept. To put it simply, the word itself - the combination of sounds and letters has absolutely NOTHING to do with the thing it symbolizes. But ...
PW-E300 Operation
PW-E300 Operation

... law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press. • The data content of each Dictionary is mostly retained faithful to the original. How ...
Chapter 15
Chapter 15

... Recognition (perceptual task) vs. comprehension (involves retrieval of additional info from memory) Damage to the L temporal lobe can produce pure word deafness (ability to hear, speak, and usually to read and write without being able to comprehend the meaning of speech) without disrupting other fun ...
Prep., Conj. & Interj.
Prep., Conj. & Interj.

... between a noun or pronoun and some other word in the sentence. • Robots in outer space perform useful functions. • The robot is above the spacecraft. ...
Magnifico: A Platform For Expert Mining Using Metadata
Magnifico: A Platform For Expert Mining Using Metadata

... The search result is presented in part 3 of the page, sorted by the Magnifico score. 10 people are loaded each time to increase the performance and improve the user experience of the system. More people will be loaded if the page is scrolled to the bottom. For each person having a Mendeley profile, ...
An Accurate Arabic Root-Based Lemmatizer for
An Accurate Arabic Root-Based Lemmatizer for

... tense forms, because they retain some affixes and internal differences. On the other hand, statistical supervised learning approaches present the best published accuracy as POS taggers. The cost of expanding language coverage is a major problem in supervised learning approaches. In closed learning m ...
PC-Kimmo
PC-Kimmo

... Word Grammar Rules The grammar uses context-free rules consisting of a nonterminal symbol on the left side of the rule which is expanded into one or more symbols on the right side. Word Word Word ...
The Analysis
The Analysis

... objects are easily perceived by the senses while abstract notions are perceived by the mind. When an abstract notion is by the force of the mind represented through a concrete object, an image is the result (ibid: 31). Lexical meaning is a means by which a word-form is made to express a definite con ...
Semantics-Based Spam Detection by Observance of Outgoing
Semantics-Based Spam Detection by Observance of Outgoing

... 2) Synsets Generation The goal of WordNet is the creation of dictionary and thesaurus which could be used intuitively. The next purpose of WordNet is the support for automatic text analysis and artificial intelligence. WordNet is a lexical database for English language. It groups English words into ...
lecture24 - University of Arizona
lecture24 - University of Arizona

... – 19. Montague Grammar and Machine Translation. Landsbergen, J. – 20. Dialogue Translation vs. Text Translation – Interpretation Based Approach. Tsujii, J.-I. And M. Nagao – 21. Translation by Structural Correspondences. Kaplan, R. et al. – 22. Pros and Cons of the Pivot and Transfer Approaches in M ...
NMRC CRA Question Paper 2-2015
NMRC CRA Question Paper 2-2015

... GENERAL ENGLISH DIRECTIONS: (Question No. 1 to 7) The passage given below is followed by ten questions. Each question has four alternative answers, out of which only one is correct. Write the serial number of correct answer (1), (2), (3) and (4) in the answer-sheet. I read the other day some verses ...
< 1 ... 11 12 13 14 15 16 17 18 19 ... 42 >

Word-sense disambiguation

In computational linguistics, word-sense disambiguation (WSD) is an open problem of natural language processing and ontology. WSD is identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings. The solution to this problem impacts other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, inference et cetera.The human brain is quite proficient at word-sense disambiguation. The fact that natural language is formed in a way that requires so much of it is a reflection of that neurologic reality. In other words, human language developed in a way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning.To date, a rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful algorithms to date.Current accuracy is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (homograph) level is routinely above 90%, with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in recent evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively.
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