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Textual entailment graphs



In this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.



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Adler, M., Berant, J., and Dagan, I. 2012. Entailment-based text exploration with application to the health-care domain. In Proceedings of the ACL 2012 System Demonstrations, Association for Computational Linguistics, pp. 79–84.
Aharoni, E., Alzate, C., Bar-Haim, R., Bilu, Y., Dankin, L., Eiron, I., Hershcovich, D., and Hummel, S. 2014a. Claims on demand–an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora. In COLING 2014.
Aharoni, E., Polnarov, A., Lavee, T., Hershcovich, D., Levy, R., Rinott, R., Gutfreund, D., and Slonim, N. 2014b. A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In ACL 2014.
Androutsopoulos, I., and Prodromos, M.,2010. A survey of paraphrasing and textual entailment methods. Journal of Artificial Intelligence Research 38 (1): 135187.
Bentivogli, L., Clark, P., Dagan, I., Dang, H., and Giampiccolo, D. 2011. The seventh pascal recognizing textual entailment challenge. In Proceedings of TAC, 2011.
Bentivogli, L., Clark, P., Dagan, I., and Giampiccolo, D. 2010. The sixth pascal recognizing textual entailment challenge. In Proceedings of TAC, 2010.
Berant, J., Dagan, I., and Goldberger, J. 2011. Global learning of typed entailment rules. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, Association for Computational Linguistics, pp. 610–619.
Berant, J., Dagan, I., and Goldberger, J., 2012. Learning entailment relations by global graph structure optimization. Computational Linguistics 38 (1): 73111.
Chklovski, T., and Pantel, P. 2004. Verbocean: mining the web for fine-grained semantic verb relations. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP-2004), pp. 33–40.
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37.
Dagan, I., Dolan, B., Magnini, B., and Roth, D. 2010. Recognizing textual entailment: rational, evaluation and approaches–erratum. Natural Language Engineering 16 (01).
Dagan, I., and Glickman, O. 2004. Probabilistic textual entailment: generic applied modeling of language variability.
Dagan, I., Glickman, O., and Magnini, B. 2006. The pascal recognising textual entailment challenge. In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, Springer, pp. 177–190.
Fellbaum, C. 1998. WordNet. Wiley Online Library.
Harabagiu, S., and Hickl, A., 2006. Methods for using textual entailment in open-domain question answering. In ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Morristown, NJ, USA, pp. 905912.
Harabagiu, S. M., Andrew, H., and Lacatusu, V. L., 2007. Satisfying information needs with multi-document summaries. Information Processing and Management 43 (6): 16191642.
Landis, J. R., and Koch, G. G., 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1): 159174.
Levy, O., Dagan, I., and Goldberger, J. 2014. Focused entailment graphs for open ie propositions. In Conference on Computational Natural Language Learning.
Lloret, E., Ferràndez, O., Muñoz, R., and Palomar, M. 2008. A text summarization approach under the influence of textual entailment. In Sharp, B., and Zock, M., (eds.), NLPCS, pp. 22–31. INSTICC.
Magnini, B., Zanoli, R., Dagan, I., Eichler, K., Neumann, G., Noh, T.-G., Pado, S., Stern, A., and Levy, O. 2014. The excitement open platform for textual inferences. In Proceedings of ACL.
McNemar, Q., 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12 (2): 153157.
Mehdad, Y., Giuseppe, C., Ng, R. T., and Joty, S. R. 2013. Towards topic labeling with phrase entailment and aggregation. In Proceedings of NAACL-HLT, pp. 179–189.
Mihalcea, R., Corley, C., and Strapparava, C. 2006. Corpus-based and knowledge-based measures of text semantic similarity. In AAAI, Volume 6, pp. 775–780.
Mirkin, S., Specia, L., Cancedda, N., Dagan, I., Dymetman, M., and Szpektor, I. 2009. Source-language entailment modeling for translating unknown terms. In Proceedings of ACL-IJCNLP.
Miyao, Y., Shima, H., Kanayama, H., and Mitamura, T., 2012. Evaluating textual entailment recognition for university entrance examinations. ACM Transactions on Asian Language Information Processing (TALIP) 11 (4): 13.
Nakashole, N., Weikum, G., and Suchanek, F. 2012. Patty: a taxonomy of relational patterns with semantic types. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics, pp. 1135–1145.
Negri, M., Marchetti, A., Mehdad, Y., Bentivogli, L., and Giampiccolo, D. 2012. Semeval-2012 task 8: cross-lingual textual entailment for content synchronization. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the 6th International Workshop on Semantic Evaluation, Association for Computational Linguistics, pp. 399–407.
Nielsen, R. D., Ward, W., and Martin, J. H., 2009. Recognizing entailment in intelligent tutoring systems. Natural Language Engineering 15 (04): 479501.
Slonim, N., Ran, L., Yonatan, B., Daniel, H., and Ehud, A. 2014. Context dependent claim detection. In COLING 2014.
Snow, R., Jurafsky, D., and Ng, A. Y. 2006. Semantic taxonomy induction from heterogenous evidence. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, pp. 801–808.
Stern, A., and Dagan, I. 2013. The biutee research platform for transformation-based textual entailment recognition. Linguistic Issues in Language Technology 9.
Suchanek, F. M., Kasneci, G., and Weikum, G., 2008. Yago: a large ontology from wikipedia and wordnet. Web Semantics: Science, Services and Agents on the World Wide Web 6 (3): 203217.
Wang, R., and Neumann, G. 2008. An divide-and-conquer strategy for recognizing textual entailment. In Proceedings of the Text Analysis Conference, Gaithersburg, MD.
Zanzotto, F. M., Pennacchiotti, M., and Tsioutsiouliklis, K. 2011. Linguistic redundancy in twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 659–669.
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Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
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