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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3944))

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Abstract

In this paper we present a classifier for Recognising Textual Entailment (RTE) and Semantic Equivalence. We evaluate the performance of this classifier using an evaluation framework provided by the PASCAL RTE Challenge Workshop. Sentence–pairs are represented as a set of features, which are used by our decision tree classifier to determine if an entailment relationship exisits between each sentence–pair in the RTE test corpus.

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References

  1. Dagan, I., Glickman, O., Magnini, B. (eds.): Proceedings of the PASCAL Recognising Textual Entailment Challenge Workshop, Southampton, UK, April 11-13 (2005)

    Google Scholar 

  2. Radev, D.: Summarisation Tutorial. In: SIGIR 2004 (2004), http://www.summarization.com/sigirtutorial2004.ppt

  3. Dolan, B., Dagan, I. (eds.): Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan, USA, June 30 (2005)

    Google Scholar 

  4. Corley, C., Mihalcea, R.: Measuring the Semantic Similarity of Texts. In: Proceedings of ACL Workshop on Empirical Modelling of Semantic Equivalence and Entailment. ACL (June 2005)

    Google Scholar 

  5. Budanitsky, A., Hirst, G.: Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. In: Proceedings of Workshop on WordNet and Other Lexical Resources, Second meeting of the North American. Chapter of the Association for Computational Linguistics (2001)

    Google Scholar 

  6. de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., Sammons, M.: An Inference Model for Semantic Entailment in Natural Language. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  7. Akhmatova, E.: Textual Entailment Resolution via Atomic Propositions. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  8. Bos, J., Markert, K.: Combining Shallow and Deep NLP methods for Recognizing Textual Entailment. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  9. Fowler, A., Hauser, B., Hodges, D., Niles, I., Novischi, A., Stephan, J.: Applying COGEX to Recognize Textual Entailment. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  10. Pazienza, M.T., Pennacchiotti, M., Zanzotto, F.M.: Textual Entailment as Syntactic Graph Distance. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  11. Herrera, J., Peñas, A., Verdejo, F.: Textual Entailment Recognition based on dependency analysis and WordNet. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  12. Vanderwende, L., Coughlin, D., Dolan, W.: What Syntax can Contribute in Entailment Task. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  13. Marsi, E., Krahmer, E.: Classification of semantic relations by humans and machines. In: Proc. ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor (June 2005)

    Google Scholar 

  14. Raina, R., et al.: Robust Textual Inference using Diverse Knowledge Sources. In: Proc. PASCAL Workshop on Recognising Textual Entailment (2005)

    Google Scholar 

  15. Raina, R., Ng, A.Y., Manning, C.D.: Robust Textual Inference via Learning and Abductive Reasoning. AAAI, Menlo Park (2005)

    Google Scholar 

  16. van Rijsbergen, C.J.: Information Retrieval, http://www.dcs.gla.ac.uk/Keith/Preface.html

  17. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York (1999)

    Google Scholar 

  18. Hatzivassiloglou, V., et al.: SimFinder: A Flexible Clustering Tool for Summarization. In: Workshop on Automatic Summarization, NAACL, Pittsburg, USA (2001)

    Google Scholar 

  19. Carbonell, J., Goldstein, J.: The use of MMR, Diversity–Based Reranking for Reordering Documents and Producing Summaries. In: SIGIR 1998, Melbourne, Australia (1998)

    Google Scholar 

  20. Goldstein, J., Mittal, V., Carbonell, J., Kantrowitz, M.: Multi–Document Summarization by Sentence Extraction. Automatic Summarization. In: Proceedings of the ANLP/NAACL Workshop, Seattle, WA (April 2000)

    Google Scholar 

  21. Allan, J., Gupta, R., Khandewal, V.: Temporal Summaries of News Topics. In: Proceedings of SIGIR 2001 (2001)

    Google Scholar 

  22. Barzilay, R., McKeown, K.R.: Sentence Fusion for Multidocument News Summarization. Computational Linguistics (2005)

    Google Scholar 

  23. Barzilay, R., Elhadad, N.: Sentence Alignment for Monolingual Comparable Corpora. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP), Sapporo, Japan (2003)

    Google Scholar 

  24. Barzilay, R.: Multidocument Summarizer, PhD Thesis. Columbia University (2002)

    Google Scholar 

  25. Melcuk, I.: Dependency Syntax: Theory and Practice. State of New York University Press, Albany

    Google Scholar 

  26. NewsBlaster: Columbia University (2005), http://newsblaster.cs.columbia.edu/

  27. Quinlan, J.R.: C5.0 Machine Learning Algorithm, At: http://www.rulequest.com

  28. Miller, G.A., et al.: WordNet: Lexical Database for the English language. Cognitive Science Laboratory. Princeton University, At: http://www.cogsci.princeton.edu/~wn

  29. Chklovski, T., Pantel, P.: VerbOcean: Mining the Web for Fine–Grained Semantic Verb Relations. In: Proc. Conf. Empirical Methods in Natural Language Processing, EMNLP 2004 (2004)

    Google Scholar 

  30. Deerwester, S., Dumais, S.T., Furna, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science (1990)

    Google Scholar 

  31. Landauer, T.K., Foltz, P.W., Latham, D.: Introduction to Latent Semantic Analysis. Discourse Processes (1998)

    Google Scholar 

  32. Lin, C.-Y., Hovy, E.: Automatic Evaluation of Summaries using n-gram co– occurence statistics. In: Proc. Document Understanding Conference (DUC), National Institute of Standards and Technology (2004)

    Google Scholar 

  33. Patwardhan, S., Michelizzi, J., Banerjee, S., Pedersen, T.: WordNet:Similarity Perl Module, http://search.cpan.org/dist/WordNet-Similarity/lib/WordNet/Similarity.pm

  34. Rennie, J.: WordNet:QueryData Perl Module, At: http://search.cpan.org/~jrennie/WordNet-QueryData-1.39/QueryData.pm

  35. Document Understanding Conference (DUC), National Institute of Standards and Technology, USA, At: http://duc.nist.gov

  36. Porter, M.: An Algorithm for Suffix Stripping. Progam 14(3) (July 1980), At: http://www.tartarus.org/~martin/PorterStemmer/def.txt

  37. Galassi, M., et al.: GNU Scientific Library Reference Manual, 2nd edn., At: http://www.gnu.org/software/gsl/

  38. Chklovski, T., Pantel, P.: Global Path-based Refinement of Noisy Graphs Applied to Verb Semantics. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 792–803. Springer, Heidelberg (2005)

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© 2006 Springer-Verlag Berlin Heidelberg

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Newman, E., Stokes, N., Dunnion, J., Carthy, J. (2006). Textual Entailment Recognition Using a Linguistically–Motivated Decision Tree Classifier. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_21

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  • DOI: https://doi.org/10.1007/11736790_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33427-9

  • Online ISBN: 978-3-540-33428-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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