Home Nonfiction 7 • Dan Cristea, Emanuel Dima, Corina Dima (auth.), Sobha's Anaphora Processing and Applications: 7th Discourse Anaphora PDF

Dan Cristea, Emanuel Dima, Corina Dima (auth.), Sobha's Anaphora Processing and Applications: 7th Discourse Anaphora PDF

By Dan Cristea, Emanuel Dima, Corina Dima (auth.), Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)

ISBN-10: 3642049745

ISBN-13: 9783642049743

This ebook constitutes the refereed court cases of the seventh Discourse Anaphora and Anaphor answer Colloquium, DAARC 2009, held in Goa, India, in November 2009.

The 10 revised complete papers awarded have been conscientiously reviewed and chosen from 37 preliminary submissions. The papers are equipped in topical sections on answer method, computational purposes, language research, and human processing.

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Read Online or Download Anaphora Processing and Applications: 7th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2009 Goa, India, November 5-6, 2009 Proceedings PDF

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Extra resources for Anaphora Processing and Applications: 7th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2009 Goa, India, November 5-6, 2009 Proceedings

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Computational Linguistics 20(4), 535–561 (1994) 5. : A mentionsynchronous coreference resolution algorithm based on the Bell tree. In: Proceedings of ACL, pp. 21–26 (2004) 6. : Using decision trees for coreference resolution. In: Proceedings of IJCAI, pp. 1050–1055 (1995) 7. : Robust pronoun resolution with limited knowledge. In: Proceedings of ACL-COLING, pp. 869–875 (1998) 8. : Using coreference in question answering. In: Proceedings of the 8th Text REtrieval Conference, pp. 85–89 (1999) 9. : Improving machine learning approaches to coreference resolution.

Lalitha Devi, A. Branco, and R. ): DAARC 2009, LNAI 5847, pp. 29–42, 2009. c Springer-Verlag Berlin Heidelberg 2009 30 M. Recasens and E. Hovy This paper focuses on the classification stage and, in particular, on (i) the features that are used to build the feature vector that represents a pair of mentions,1 and (ii) the selection of positive and negative training instances. The choice of the information encoded in the feature vectors is of utmost importance as they are the basis on which the machine learning algorithm learns the pairwise coreference model.

Memory-Based Language Processing. Cambridge University Press, Cambridge (2005) 3. : Optimization Issues in Machine Learning of Coreference Resolution. D thesis, University of Antwerp (2005) 4. : An algorithm for pronominal anaphora resolution. Computational Linguistics 20(4), 535–561 (1994) 5. : A mentionsynchronous coreference resolution algorithm based on the Bell tree. In: Proceedings of ACL, pp. 21–26 (2004) 6. : Using decision trees for coreference resolution. In: Proceedings of IJCAI, pp.

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Anaphora Processing and Applications: 7th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2009 Goa, India, November 5-6, 2009 Proceedings by Dan Cristea, Emanuel Dima, Corina Dima (auth.), Sobha Lalitha Devi, António Branco, Ruslan Mitkov (eds.)


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