Abstract
We propose a novel approach to recognise textual entailment (RTE) following a two-stage architecture – alignment and decision – where both stages are based on semantic representations. In the alignment stage the entailment candidate pairs are represented and aligned using predicate-argument structures. In the decision stage, a Markov Logic Network (MLN) is learnt using rich relational information from the alignment stage to predict an entailment decision. We evaluate this approach using the RTE Challenge datasets. It achieves the best results for the RTE-3 dataset and shows comparable performance against the state of the art approaches for other datasets.
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References
Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. J. Artif. Int. Res. 38(1), 135–187 (2010)
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., Szpektor, I.: The second pascal recognising textual entailment challenge. In: Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy (2006)
Beltagy, I., Chau, C., Boleda, G., Garrette, D., Erk, K., Mooney, R.: Montague meets markov: Deep semantics with probabilistic logical form. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity, Atlanta, Georgia, USA, vol. 1, pp. 11–21 (June 2013)
Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., de Marneffe, M.C., Ramage, D., Yeh, E., Manning, C.D.: Learning alignments and leveraging natural logic. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 165–170. Association for Computational Linguistics, Prague (2007)
Chklovski, T., Pantel, P.: Verbocean: Mining the web for fine-grained semantic verb relations. In: Proceedings of EMNLP 2004, pp. 33–40 (2004)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.P.: Natural language processing (almost) from scratch. Journal of Machine Learning Research 12, 2493–2537 (2011)
Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006)
Garrette, D., Erk, K., Mooney, R.: Integrating logical representations with probabilistic information using Markov logic. In: Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011), pp. 105–114 (2011)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press (2007)
Giampiccolo, D., Magnini, B., Dagan, I., Dolan, B.: The third pascal recognizing textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, pp. 1–9 (2007)
Glickman, O., Dagan, I., Koppel, M.: A lexical alignment model for probabilistic textual entailment. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 287–298. Springer, Heidelberg (2006)
Harmeling, S.: An extensible probabilistic transformation-based approach to the third recognizing textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 137–142. Association for Computational Linguistics, Prague (2007)
Kotlerman, L., Dagan, I., Szpektor, I., Zhitomirsky-geffet, M.: Directional distributional similarity for lexical inference. Nat. Lang. Eng. 16(4), 359–389 (2010)
Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Montréal, Canada, pp. 768–774 (1998)
MacCartney, B., Galley, M., Manning, C.D.: A phrase-based alignment model for natural language inference. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 802–811. Association for Computational Linguistics, Honolulu (2008)
MacCartney, B., Manning, C.D.: Natural logic for textual inference. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 193–200. Association for Computational Linguistics, Prague (2007)
de Marneffe, M.C., MacCartney, B., Grenager, T., Cer, D., Rafferty, A., Manning, C.D.: Learning to distinguish valid textual entailments. In: Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy (2006)
Mehdad, Y., Magnini, B.: A word overlap baseline for the recognizing textual entailment task (2009), http://hlt.fbk.eu/sites/hlt.fbk.eu/files/baseline.pdf
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107–136 (2006)
Rios, M., Aziz, W., Specia, L.: TINE: A metric to assess MT adequacy. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, Edinburgh, Scotland, pp. 116–122 (2011)
Rios, M., Aziz, W., Specia, L.: UOW: Semantically informed text similarity. In: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), Montréal, Canada, pp. 673–678 (2012)
Wang, M., Manning, C.D.: Probabilistic tree-edit models with structured latent variables for textual entailment and question answering. In: Proceedings of the 23rd International Conference on Computational Linguistics, COLING 2010, Stroudsburg, PA, USA, pp. 1164–1172 (2010)
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Rios, M., Specia, L., Gelbukh, A., Mitkov, R. (2014). Statistical Relational Learning to Recognise Textual Entailment. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54906-9_27
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DOI: https://doi.org/10.1007/978-3-642-54906-9_27
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