Textual entailment recognition using a linguistically-motivated decision tree classifier

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3944 LNAI, Issue , 2006, Pages 372-384

  Newman, Eamonn ^a   Stokes, Nicola ^b   Dunnion, John ^a   Carthy, Joe ^a  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DECISION SUPPORT SYSTEMS; PASCAL (PROGRAMMING LANGUAGE); PATTERN RECOGNITION; SEMANTICS; TREES (MATHEMATICS);

DECISION TREE CLASSIFIERS; ENTAILMENT RECOGNITION; RECOGNISING TEXTUAL ENTAILMENT (RTE);

TEXT PROCESSING;

EID: 33745855868     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11736790_21     Document Type: Conference Paper

References (38)

1. Dagan, I., Glickman, O., Magnini, B.(eds): Proceedings of the PASCAL Recognising Textual Entailment Challenge Workshop. April 11th-13th 2005, Southampton, UK.
2. Radev, D.: Summarisation Tutorial. SIGIR 2004. At http://www.summarization.com/sigirtutorial2004.ppt
3. Dolan, B., and Dagan, I. (eds): Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment. June 30th 2005, Ann Arbor, Michigan, USA.
4. Corley, C., and Mihalcea, R.: Measuring the Semantic Similarity of Texts. In Proceedings of ACL Workshop on Empirical Modelling of Semantic Equivalence and Entailment, ACL, June 2005.
5. Budanitsky A. arid 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.
6. de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D. and Sammons, M: An Inference Model for Semantic Entailment in Natural Language. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
7. Akhmatova, E.: Textual Entailment Resolution via Atomic Propositions. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
8. Bos, J. and Markert, K.: Combining Shallow and Deep NLP methods for Recognizing Textual Entailment. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
9. Fowler, A., Hauser, B., Hodges, D., Niles, I., Novischi, A., and Stephan J.: Applying COGEX to Recognize Textual Entailment. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
10. Pazienza, M. T., Pennacchiotti, M., Zanzotto, F. M.: Textual Entailment as Syntactic Graph Distance. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
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.
12. Vauderwende, L., Coughlin, D., Dolan, W.: What Syntax can Contribute in Entailment Task. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
13. Marsi, E. and 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.
14. Raina, R., et al.: Robust Textual Inference using Diverse Knowledge Sources. In Proc. PASCAL Workshop on Recognising Textual Entailment, 2005.
15. Raina, R., Ng, A. Y., Manning, C. D.: Robust Textual Inference via Learning and Abductive Reasoning. AAAI, 2005.
16. van Rijsbergen, C. J.: Information Retrieval, http://www.dcs.gla.ac.uk/Keith/Preface.html
17. Baeza-Yates, R. and Ribeiro-Neto, B.: Modern Information Retrieval, ACM Press, 1999.
18. Hatzivassiloglou, V., et al.: SIMFINDER: A Flexible Clustering Tool for Summarization. In Workshop on Automatic Summarization, NAACL, Pittsburg, USA, 2001.
19. Carbonell, J., and Goldstein, J.: The use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR, 1998, Melbourne, Australia.
20. Goldstein, J., Mittal, V., Carbonell, J., and Kantrowitz, M.: Multi-Document Summarization by Sentence Extraction. Automatic Summarization, Proceedings of the ANLP/NAACL Workshop, April 2000, Seattle, WA
21. Allan, J., Gupta, R., and Khandewal, V.: Temporal Summaries of News Topics. In Proceedings of SIGIR 2001.
22. Barzilay, R and McKeown, K. R.: Sentence Fusion for Multidocument News Summarization. Computational Linguistics, 2005.
23. Barzilay, R. and Elhadad, N.: Sentence Alignment for Monolingual Comparable Corpora. Proceedings of Empirical Methods in Natural Language Processing (EMNLP), Sapporo, Japan, 2003.
24. Barzilay, R.: Multidocument Summarizer, PhD Thesis, 2002, Columbia University.
25. Melcuk, I.: Dependency Syntax: Theory and Practice, Albany, State of New York University Press.
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. Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP-04), 2004.
30. Deerwester, S., Dumais, S. T., Furna, G. W., Landauer, T. K., and Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 1990.
31. Landauer, T.K., Foltz, P.W., Latham, D: Introduction to Latent Semantic Analysis. Discourse Processes, 1998.
32. Lin, C.-Y., Hovy, E.: Automatic Evaluation of Summaries using n-gram co-occurence statistics. Proc. Document Understanding Conference (DUC), National Institute of Standards and Technology, 2004.
33. Patwardhan, S., Michelizzi, J., Banerjee, S., and Pedersen, T.: WordNet::Similarity Perl Module At 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, in Progam, vol. 14, no. 3, July 1980. At http://www.tartarus.org/~martin/PorterStemmer/def.txt.
37. Galassi, M., et al,: GNU Scientific Library Reference Manual (2nd Ed.) At http://www.gnu.org/software/gsl/
38. Chklovski, T., Pantel, P.: Global Path-based Refinement of Noisy Graphs Applied to Verb Semantics. In Proceedings of The Second International Joint Conference on Natural Language Processing (IJCNLP-05), Jeju Island, South Korea, October 11-13, 2005.