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Get Computational Linguistics and Intelligent Text Processing: PDF

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

This two-volume set, inclusive of LNCS 6608 and LNCS 6609, constitutes the completely refereed lawsuits of the twelfth foreign 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 based on the subsequent 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; computing device translation and multilingualism; details extraction and knowledge retrieval; textual content categorization and category; summarization and spotting textual entailment; authoring relief, errors correction, and elegance research; and speech attractiveness 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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A CP is generally made via the combination of nouns, adjectives and verbs with other verbs. The verb in the CP is referred as light verb and the element that the light verb combines to form a CP is referred as host [4]. e. most of the meaning the complex predicate has. [6] has talked about four types of complex predicates: (a) In Syntactic Complex Predicates the formation takes place in the syntax. (b) In Morphological Complex Predicates, a piece of morphology is used to modify the primary event predication.

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. But previous studies gave negative answer. [11] examined a context-based method but only achieved 34% accuracy.

We experiment on a Chinese thesaurus Cilin and extract context for test words from a large corpus to validate the effectiveness of the proposed approach. We also compare several weighting schemes in measuring contextual similarity. The remainder of this paper is organized as follows. Section 2 introduces related work on supersense tagging. The proposed approach is described in Section 3. Experiments results and error analysis are presented in Section 4 and Section 5 respectively. The final section gives out conclusion and presents future work.

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