New PDF release: Computational Linguistics and Intelligent Text Processing:

By Alexander Gelbukh

ISBN-10: 3642549055

ISBN-13: 9783642549052

ISBN-10: 3642549063

ISBN-13: 9783642549069

This two-volume set, along with LNCS 8403 and LNCS 8404, constitutes the completely refereed court cases of the 14th overseas convention on clever textual content Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The eighty five revised papers provided including four invited papers have been rigorously reviewed and chosen from three hundred submissions. The papers are prepared within the following topical sections: lexical assets; record illustration; morphology, POS-tagging, and named entity attractiveness; syntax and parsing; anaphora answer; spotting textual entailment; semantics and discourse; usual language new release; sentiment research and emotion attractiveness; opinion mining and social networks; desktop translation and multilingualism; info retrieval; textual content class and clustering; textual content summarization; plagiarism detection; kind and spelling checking; speech processing; and applications.

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Extra info for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I

Example text

We then make some proposals in order to get better results using a finer grain model of constraints. 3 ASSCI, A State-of-the Art Subcategorization Acquisition System for French A system for the automatic acquisition of sub-categorization frames has recently been implemented for French. This system called ASSCI is capable of acquiring large scale lexicons from un-annotated corpora [19]. This system is close to other systems developed for example for English [15,20] in that it extracts SCFs from data parsed using a shallow dependency parser [21] and is capable of identifying a large number of SCFs.

As natural languages are complex, lexical acquisition needs to take into account a wide range of parameters and constraints. However, surprisingly, in the acquisition community, relatively few investigations have been done on the structure of the linguistic constraints themselves, beyond the engineering point of view (but note that this work has been extensively done for parsing, see [1]). In this paper, we want to take another look at some experiments recently done on the automatic acquisition of lexical resources from textual corpora, more specifically on French.

The Minimalist Program. The MIT Press, Cambridge (1995) 7. : Can subcategorisation probabilities help a statistical parser? In: Proceedings of the 6th ACL/SIGDAT Workshop on Very Large Corpora, Montreal, Canada (1998) 8. : Lexicalization in crosslinguistic probabilistic parsing: The case of French. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 306–313. Association for Computational Linguistics, Ann Arbor (2005) 9. : Inducing German Semantic Verb Classes from Purely Syntactic Subcategorisation Information.

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Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I by Alexander Gelbukh


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