Computational Linguistics and Intelligent Text Processing: - download pdf or read online

By Eduard Bejček, Pavel Straňák, Daniel Zeman (auth.), Alexander F. Gelbukh (eds.)

This two-volume set, together with LNCS 6608 and LNCS 6609, constitutes the completely refereed lawsuits of the twelfth overseas convention on laptop Linguistics and clever Processing, held in Tokyo, Japan, in February 2011. The seventy four complete papers, offered including four invited papers, have been rigorously reviewed and chosen from 298 submissions. The contents were ordered in line with the next topical sections: lexical assets; syntax and parsing; part-of-speech tagging and morphology; note feel disambiguation; semantics and discourse; opinion mining and sentiment detection; textual content new release; laptop translation and multilingualism; info extraction and knowledge retrieval; textual content categorization and class; summarization and spotting textual entailment; authoring relief, errors correction, and elegance research; and speech popularity and generation.

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Extra resources for Computational Linguistics and Intelligent Text Processing: 12th International Conference, CICLing 2011, Tokyo, Japan, February 20-26, 2011. Proceedings, Part I

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The word order differences are a good reason why MWE detection should be done on dependency trees (as opposed to simple bracketing). Instead of looking at sequences of adjacent tokens, one can query parent-child pairs that remain the same regardless of word order. See Figure 4 for the dependency trees of (16) and (17). 4 Discontinuity on Surface Discontinuous MWEs pose a problem similar to the word-order issue. Even a very lexicalised phrase (such as a verbal phraseme) can be disconnected with other words breaking in.

Wu, and Y. Shao However, pilot studies on Chinese unknown words [7, 9, 10] mainly used structural information. The experiments of these studies show the effectiveness of structural information for Chinese. , means ‘person’. , w1 = shi-min ‘citizen’. Structure-based methods can be used in Chinese but not English, because most Chinese characters are meaningful while English characters are not. Then, one question is, how about using context-based method in Chinese? Since contextual information has been proved effective in English, it is reasonable to believe that it should also be effective in Chinese, as distributional hypothesis seems do not have difference among languages.

It is used to measure the contextual similarity between two words. Structure-based approach is tightlycoupled with context-based approach. Firstly, the structure-based approach generates a candidate synonym set. The context-based method would be used to measure the similarity between the test word and each of the word among the candidate synonym set. Secondly, the structure-based approach adjusts the similarity scores computed by context-based approach. The basic idea of the using of structural information in this strategy is the assumption that: the more common characters two Chinese words share, the closer their meanings are.

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Computational Linguistics and Intelligent Text Processing: 12th International Conference, CICLing 2011, Tokyo, Japan, February 20-26, 2011. Proceedings, Part I by Eduard Bejček, Pavel Straňák, Daniel Zeman (auth.), Alexander F. Gelbukh (eds.)

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