Abstract
This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of the model on the Recognising Textual Entailment challenge dataset along with some analysis.
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References
Bacchus, F.: Representing and Reasoning with Probabilistic Knowledge. MIT Press, Cambridge (1990)
Bar-Haim, R., Szpektor, I., Glickman, O.: Definition and Analysis of Intermediate Entailment Levels. In: ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005)
Bos, J., Markert, K.: Recognising Textual Entailment with Robust Logical Inference. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 404–426. Springer, Heidelberg (2006)
Brown, P.F., Della Pietra, V.J., Della Pietra, S.A., Mercer, R.L.: The Mathematics of Statistical Machine Translation: Parameter Estimation. Computational Linguistics 19(2), 263–311 (1993)
Chierchia, G., McConnell-Ginet, S.: Meaning and grammar: An introduction to semantics, 2nd edn. MIT Press, Cambridge (2001)
Corley, C., Mihalcea, R.: Measuring the Semantic Similarity of Texts. In: ACL 2005 Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005)
Dagan, I., Glickman, O.: Probabilistic Textual Entailment: Generic Applied Modeling of Language Variability. In: PASCAL workshop on Learning Methods for Text Understanding and Mining (2004)
Dagan, I., Glickman, O., Magnini, B.: The PASCAL Recognising Textual Entailment Challenge. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 404–426. Springer, Heidelberg (2006)
Geffet, M., Dagan, I.: Feature Vector Quality and Distributional Similarity. In: Coling 2004 (2004)
Geffet, M., Dagan, I.: The Distributional Inclusion Hypotheses and Lexical Entailment. In: ACL 2005 (2005)
Glickman, O., Dagan, I.: Identifying Lexical Paraphrases From a Single Corpus: A Case Study for Verbs. In: Recent Advantages in Natural Language Processing, RANLP (2003)
Glickman, O., Dagan, I., Koppel, M.: A Probabilistic Classification Approach for Lexical Textual Entailment. In: Twentieth National Conference on Artificial Intelligence, AAAI (2005)
Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990)
Jijkoun, V., de Rijke, M.: Recognizing Textual Entailment: Is Lexical Similarity Enough? In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 449–460. Springer, Heidelberg (2006)
Leacock, C., Chodorow, M., Miller, G.: Using corpus statistics and wordnet relations for sense disambiguation. Computational Linguistics 24(1), 147–165 (1998)
Lin, D.: Automatic Retrieval and Clustering of Similar Words. In: COLING-ACL (1998)
Miller, G.A.: WordNet: A Lexical Databases for English. CACM 38(11), 39–41 (1995)
Monz, C., de Rijke, M.: Light-Weight Entailment Checking for Computational Semantics. In: Proc. of the third workshop on inference in computational semantics, ICoS-3 (2001)
Ponte, J.M., Croft, W.B.: A Language Modeling Approach to Information Retrieval. In: SIGIR conference on Research and Development in Information Retrieval (1998)
Saggion, H., Gaizauskas, R., Hepple, M., Roberts, I., Greenwood, M.: Exploring the Performance of Boolean Retrieval Strategies for Open Domain Question Answering. In: Proceedings of the Workshop on Information Retrieval for Question Answering, SIGIR 2004 (2004)
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Glickman, O., Dagan, I., Koppel, M. (2006). A Lexical Alignment Model for Probabilistic Textual Entailment. 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_16
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DOI: https://doi.org/10.1007/11736790_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
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