Abstract
In this paper we define a measure for textual entailment recognition based on structural isomorphism theory applied to lexical dependency information. We describe the experiments carried out to estimate measure’s parameters with logistic regression and SVM. The results obtained show how a model constructed around lexical relationships is a plausible alternative for textual entailment recognition.
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References
Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: Pascal workshop on Learning Methods for Texts Understanding and Mining, Grenoble, France, pp. 26–29 (2004)
Pazienza, M., Pennacchiotti, M., Zanzotto, F.: Textual entailment as syntactic graph distance: a rule based and a svm based approach. In: Pascal Challenges Workshop, Southampton, UK (2005)
Matsumoto, Y., Ishimoto, H., Utsuro, T.: Learning to paraphrase: An unsupervised approach using multiple-sequence alignment. In: 31st Annual Meeting of the Association for Computational Linguistic, pp. 23–30 (1993)
Thorsten, J.: Text categorization with support vector machines: Learning with many relevant features. In: European Conference on Machine Learning (1998)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Boss, J., Markert, K.: Recognising textual entailment with logical inference. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 628–635 (2005)
Rodrigo, A., Peñas, A., Herrera, J., Verdejo, F.: Experiments of uned at the third recognizing textual entailment challenge. In: Proceedings of the Workshop on Textual Entailment and Paraphrasing, Prague, pp. 89–94 (2007)
Lin, D., Pantel, P.: Discovery of inference rules for question-answering. Natural Language Engineering 7, 343–360 (2001)
Snow, R., Vanderwende, L., Menezes, A.: Effectively using syntax for recognizing false entailment. In: Proceedings of the HLT/NAACL, pp. 33–40 (2006)
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Uribe, D. (2008). Textual Entailment Recognition Based on Structural Isomorphism. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_20
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DOI: https://doi.org/10.1007/978-3-540-88636-5_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
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