Abstract
This paper presents a Recognizing Textual Entailment system which uses semantic distances to sentence level over WordNet to assess the impact on predicting Textual Entailment datasets. We extent word-to-word metrics to sentence level in order to best fit in textual entailment domain. Finally, we show experiments over several RTE datasets and draw conclusions about the useful of WordNet semantic measures on this task. As a conclusion, we show that an initial but average-score system can be built using only semantic information from WordNet.
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Giampiccolo, D., Dang, H., Magnini, B., Dagan, I., Cabrio, E.: The Fourth PASCAL Recognizing Textual Entailment Challenge. In: Proceedings TAC 2008 (2008)
Bentivogli, L., Dagan, I., Dang, H., Giampiccolo, D., Magnini, B.: The Fifth PASCAL Recognizing Textual Entailment Challenge. In: Proceedings TAC 2009 (2009)
Herrera, J., Penas, A., Verdejo, F.: Textual Entailment Recognition Based on Dependency Analysis and WordNet. PASCAL. In: First Challenge Workshop (2005)
Ofoghi, B., Yearwood, J.: From Lexical Entailment to Recognizing Textual Entailment Using Linguistic Resources. In: Australasian Language Technology Association Workshop (2009)
Castillo, J.: A Machine Learning Approach for Recognizing Textual Entailment in Spanish. In: NAACL, Los Angeles, USA (2010)
Li, Y., McLean, D., Bandar, Z., O’Shea, J., Crockett, K.: Sentence Similarity based on Semantic Nets and Corpus Statistics. IEEE TKDE 18(8), 1138–1150 (2006)
Li, Y., Bandar, A., McLean, D.: An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE TKDE 15(4), 871–882 (2003)
Wiemer-Hastings, P.: Adding Syntactic Information to LSA. In: Proc. 22nd Ann. Conf. Cognitive Science Soc., pp. 989–993 (2000)
Landauer, T., Foltz, P., Laham, D.: Introduction to Latent Semantic Analysis. Discourse Processes 25(2-3), 259–284 (1998)
Witten, I., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Castillo, J.: Using Machine Translation Systems to Expand a Corpus in Textual Entailment. In: Icetal 2010, Reykjavik, Iceland (2010)
Resnik, P.: Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Proc. of IJCAI 1995, pp. 448–453 (1995)
Lin, D.: An Information-Theoretic Definition of Similarity. In: Proc. of Conf. on Machine Learning, pp. 296–304 (1998)
Jiang, J., Conrath, D.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: Proc. ROCLING X (1997)
Pirrò, G., Seco, N.: Design, Implementation and Evaluation of a New Similarity Metric Combining Feature and Intrinsic Information Content. Springer, Heidelberg (2008)
Castillo, J.: Recognizing Textual Entailment: Experiments with Machine Learning Algorithms and RTE Corpora. In: Cicling 2010, Iaşi, Romania (2010)
Lesk, L.: Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from a ice cream cone. In: Proceedings of SIGDOC 1986 (1986)
Kuhn, H.: The Hungarian Method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)
Levenshtein, V.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady 10, 707 (1966)
Zanzotto, F., Pennacchiotti, M., Moschitti, A.: Shallow Semantics in Fast Textual Entailment Rule Learners, RTE3, Prague (2007)
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Castillo, J.J., Cardenas, M.E. (2010). Using Sentence Semantic Similarity Based on WordNet in Recognizing Textual Entailment. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_37
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DOI: https://doi.org/10.1007/978-3-642-16952-6_37
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