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A machine learning approach to textual entailment recognition

Published online by Cambridge University Press:  16 September 2009

FABIO MASSIMO ZANZOTTO
Affiliation:
DISP, University of Rome ‘Tor Vergata’, Roma, Italy e-mail: zanzotto@info.uniroma2.it
MARCO PENNACCHIOTTI
Affiliation:
Computerlinguistik, Universität des Saarlandes, Saarbrücken, Germany e-mail: pennacchiotti@coli.uni-sb.de
ALESSANDRO MOSCHITTI
Affiliation:
DISI, University of Trento, Povo di Trento, Italy e-mail: moschitti@disi.unitn.it

Abstract

Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so.

Type
Papers
Copyright
Copyright © Cambridge University Press 2009

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References

Abney, S. 1996. Part-of-speech tagging and partial parsing. In Bloothooft, G., Church, K., and Young, S. (eds.), Corpus-Based Methods in Language and Speech. Dordrecht: Kluwer Academic, pp. 118136.Google Scholar
Baker, C. F., Fillmore, C. J., and Lowe, J. B. 1998. The Berkeley FrameNet project. In Proceedings of COLING-ACL, Montreal, Canada.Google Scholar
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., and Magnini, I., Szpektor, B. 2006. The second pascal recognising textual entailment challenge. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy.Google Scholar
Bar-Haim, R., Szpecktor, I., and Glickman, O. 2005. Definition and analysis of intermediate entailment levels. In Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, MI.Google Scholar
Basili, R., De Cao, D., Marocco, P., and Pennacchiotti, P. 2007. Learning selectional preferences for entailment or paraphrasing rules. In Proceedings of RANLP 2007, Borovets, Bulgaria.Google Scholar
Bhagat, R., Pantel, P., and Hovy, E. 2007. Ledir: an unsupervised algorithm for learning directionality of inference rules. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-07), Prague.Google Scholar
Carpenter, B. 1992. The Logic of Typed Feature Structures. Cambridge, England, UK: Cambridge University Press.CrossRefGoogle Scholar
Carreras, X., and Màrquez, X. 2005. Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. In Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), Ann Arbor, MI.Google Scholar
Charniak, E. 2000. A maximum-entropy-inspired parser. In Proceedings of the First NAACL, Seattle, Washington, DC.Google Scholar
Collins, M., and Duffy, N. 2002. New ranking algorithms for parsing and tagging: kernels over discrete structures, and the voted perceptron. In Proceedings of ACL02, Philadelphia, PA, USA.Google Scholar
Corley, C., and Mihalcea, R. 2005. Measuring the semantic similarity of texts. In Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, MI.Google Scholar
Dagan, I., Glickman, O., and Magnini, B. 2006. The pascal recognising textual entailment challenge. In Quiñonero-Candela, J., Dagan, I., Magnini, B. and d'Alché-Buc, F. et al. (eds.), LNAI 3944: MLCW 2005, pp. 177190. Milan: Springer.Google Scholar
de Marneffe, M.-C., MacCartney, B., Grenager, T., Cer, D., Rafferty, A., and Manning, C. D. 2006. Learning to distinguish valid textual entailments. In Magnini, B. and Dagan, I. (eds.), Proceedings of the Second PASCAL Recognizing Textual Entailment Challenge. Venice: Springer, pp. 7479.Google Scholar
de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., and Sammons, M. 2005a. An inference model for semantic entailment in natural language. In Proceedings of AAAI, Pittsburgh, Pennsylvania, pp. 16781679.Google Scholar
de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., and Sammons, M. 2005b. An inference model for semantic entailment in natural language. In Proceedings of the First Pascal Challenge Workshop, Southampton, UK.Google Scholar
Giampiccolo, D., Magnini, B., Dagan, I., and Dolan, B. 2007. The third pascal recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague.Google Scholar
Gildea, D. and Jurafsky, D. 2002. Automatic Labeling of Semantic Roles. Computational Linguistics 28 (3): 245288.CrossRefGoogle Scholar
Glickman, O., Dagan, I., and Koppel, M. 2005. Web based probabilistic textual entailment. In Proceedings of the First Pascal Challenge Workshop, Southampton, UK.Google Scholar
Haasdonk, B. 2005. Feature space interpretation of SVMs with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (4): 482492.CrossRefGoogle ScholarPubMed
Haghighi, A., Ng, A., and Manning, C. 2005. Robust textual inference via graph matching. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing Vancouver, BC, Canada. Association for Computational Linguistics.Google Scholar
Harris, Z. 1964. Distributional structure. In Katz, J. J. and Fodor, J. A. (eds.), The Philosophy of Linguistics. New York: Oxford University Press, pp. 3349.Google Scholar
Hickl, A., Williams, J., Bensley Roberts, K., Rink, B., and Shi, Y. 2006. Recognizing textual entailment with LCC's groundhog system. In Magnini, B, and Dagan, I. (eds.), Proceedings of the Second PASCAL Recognizing Textual Entailment Challenge, Venice, Italy, pp. 8085.Google Scholar
Inkpen, D, Kipp., D, and Nastase, V. 2006. Machine learning experiments for textual entailment. In Magnini, B., and Dagan, I. (eds.), Proceedings of the Second PASCAL Recognizing Textual Entailment Challenge, Venice, Italy, pp. 1015.Google Scholar
Jiang, J. J., and Conrath, D. W. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the 10th ROCLING, Tapei, Taiwan.Google Scholar
Joachims, T. 1999. Making large-scale SVM learning practical. In Schlkopf, B., Burges, C., and Smola, A. (eds.), Advances in Kernel Methods-Support Vector Learning. MIT Press, Cambridge, MA, USA.Google Scholar
Katrenko, S., and Adriaans, P. 2006. Using maximal embedded syntactic subtrees for textual entailment recognition. In Magnini, B. and Dagan, I. (eds.), Proceedings of the Second PASCAL Recognizing Textual Entailment Challenge, Venice, Italy, pp. 3337.Google Scholar
Kouylekov, M., and Magnini, B. 2005. Tree edit distance for textual entailment. In Proceedings of the RANLP-2005, Borovets, Bulgaria.Google Scholar
Lin, D. and Pantel, P. 2001. DIRT – discovery of inference rules from text. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD-01), San Francisco, CA.Google Scholar
MacCartney, B., Grenager, T., de Marneffe, M.-C., Cer, D., and Manning, C. D. 2006. Learning to recognize features of valid textual entailments. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, New York City.Google Scholar
Marsi, E., Krahmer, E., and Bosma, W. 2007. Dependency-based paraphrasing for recognizing textual entailment. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague.Google Scholar
Miller, G. A. 1995. WordNet: a lexical database for English. Communications of the ACM 38 (11): 3941.CrossRefGoogle Scholar
Minnen, G., Carroll, J., and Pearce, D. 2001. Applied morphological processing of english. Natural Language Engineering 7 (3): 207223.CrossRefGoogle Scholar
Moschitti, A. 2006a. Efficient convolution kernels for dependency and constituent syntactic trees. In Proceedings of the 17th European Conference on Machine Learning, Berlin, Germany.Google Scholar
Moschitti, A. 2006b. Making tree kernels practical for natural language learning. In Proceedings of EACL'06, Trento, Italy.Google Scholar
Moschitti, A., and Zanzotto, F. M. 2007. Fast and effective kernels for relational learning from texts. In Proceedings of the International Conference of Machine Learning (ICML), Corvallis, OR.Google Scholar
Moschitti, A., and Zanzotto, F. M. 2008. Encoding tree pair-based graphs in learning algorithms: the textual entailment recognition case. In Proceedings of TextGraphs-3: Graph-Based Algorithms for Natural Language Processing Workshop Held in Coling Coference, Machester, England, UK.Google Scholar
Newman, E., Stokes, N., Dunnion, J., and Carthy, J. 2005. Textual entailment recognition using a linguistically-motivated decision tree classifier. In Candela, J. Q., Dagan, I., Magnini, B., and Buc, F. d'Alché (eds.), pp. 372–82. MLCW, Lecture Notes in Computer Science, vol. 3944. Berlin: Springer.Google Scholar
Pantel, P., Bhagat, R., Coppola, B., Chklovski, T., and Hovy, E. 2007. ISP: learning inferential selectional preferences. In Proceedings of HLT/NAACL 2007, Rochester, NY.Google Scholar
Pazienza, M. T., Pennacchiotti, M., and Zanzotto, F. M. 2005. A linguistic inspection of textual entailment. In LNAI 3673: Proceedings of the AIIA 2005, Milan.Google Scholar
Pedersen, T., Patwardhan, S., and Michelizzi, J. 2004. WordNet::Similarity – measuring the relatedness of concepts. In Proceedings of the Fifth NAACL, Boston, MA.Google Scholar
Raina, R., Haghighi, A., Cox, C., Finkel, J., Michels, J., Toutanova, K., MacCartney, B., de Marneffe, M.-C., Christopher, M., and Ng, A. Y. 2005. Robust textual inference using diverse knowledge sources. In Proceedings of the First Pascal Challenge Workshop, Southampton, UK.Google Scholar
Snow, R., Vanderwende, L., and Menezes, A. 2006. Effectively using syntax for recognizing false entailment. In Proceedings of HLT/NAACL 2006, New York.Google Scholar
Szpektor, I., Tanev, H., Dagan, I., and Coppola, B. 2004. Scaling web-based acquisition of entailment relations. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona.Google Scholar
Vanderwende, L. and Dolan, W. B. 2006. What syntax can contribute in the entailment task. In Candela, J. Q., Dagan, I., Magnini, B., and Buc, F. d'Alché (eds.), Machine Learning Challenges Workshop, pp. 205216. Lecture Notes in Computer Science, vol. 3944. Berlin: Springer.Google Scholar
Zanzotto, F. M., and Moschitti, A. 2006. Automatic learning of textual entailments with cross-pair similarities. In Proceedings of the 21st Coling and 44th ACL, Sydney.Google Scholar
Zanzotto, F. M., Pennacchiotti, M., and Pazienza, M. T. 2006. Discovering asymmetric entailment relations between verbs using selectional preferences. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Sydney.Google Scholar
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