Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T11:28:29.893Z Has data issue: false hasContentIssue false
  • English
  • Français

Textual entailment graphs

Published online by Cambridge University Press:  23 June 2015

LILI KOTLERMAN ,
IDO DAGAN ,
BERNARDO MAGNINI  and
LUISA BENTIVOGLI
LILI KOTLERMAN
Affiliation:
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel e-mail: lili.dav@gmail.com, dagan@cs.biu.ac.il
IDO DAGAN
Affiliation:
Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel e-mail: lili.dav@gmail.com, dagan@cs.biu.ac.il
BERNARDO MAGNINI
Affiliation:
Human Language Technologies Research Unit, Fondazione Bruno Kessler, Povo, Trento, Italy e-mail: magnini@fbk.eu, bentivo@fbk.eu
LUISA BENTIVOGLI
Affiliation:
Human Language Technologies Research Unit, Fondazione Bruno Kessler, Povo, Trento, Italy e-mail: magnini@fbk.eu, bentivo@fbk.eu
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Type
Articles
Information
Natural Language Engineering , Volume 21 , Special Issue 5: Graphs in NLP , November 2015 , pp. 699 - 724
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

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.Google Scholar
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.Google Scholar
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.Google Scholar
Androutsopoulos, I., and Prodromos, M.,2010. A survey of paraphrasing and textual entailment methods. Journal of Artificial Intelligence Research 38 (1): 135–187.CrossRefGoogle Scholar
Bentivogli, L., Clark, P., Dagan, I., Dang, H., and Giampiccolo, D. 2011. The seventh pascal recognizing textual entailment challenge. In Proceedings of TAC, 2011.Google Scholar
Bentivogli, L., Clark, P., Dagan, I., and Giampiccolo, D. 2010. The sixth pascal recognizing textual entailment challenge. In Proceedings of TAC, 2010.Google Scholar
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.Google Scholar
Berant, J., Dagan, I., and Goldberger, J., 2012. Learning entailment relations by global graph structure optimization. Computational Linguistics 38 (1): 73–111.CrossRefGoogle Scholar
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.Google Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37.Google Scholar
Dagan, I., Dolan, B., Magnini, B., and Roth, D. 2010. Recognizing textual entailment: rational, evaluation and approaches–erratum. Natural Language Engineering 16 (01).CrossRefGoogle Scholar
Dagan, I., and Glickman, O. 2004. Probabilistic textual entailment: generic applied modeling of language variability.Google Scholar
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.Google Scholar
Fellbaum, C. 1998. WordNet. Wiley Online Library.CrossRefGoogle Scholar
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. 905–912.Google Scholar
Harabagiu, S. M., Andrew, H., and Lacatusu, V. L., 2007. Satisfying information needs with multi-document summaries. Information Processing and Management 43 (6): 1619–1642.CrossRefGoogle Scholar
Landis, J. R., and Koch, G. G., 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1): 159–174.CrossRefGoogle ScholarPubMed
Levy, O., Dagan, I., and Goldberger, J. 2014. Focused entailment graphs for open ie propositions. In Conference on Computational Natural Language Learning.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
McNemar, Q., 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12 (2): 153–157.CrossRefGoogle ScholarPubMed
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
Nielsen, R. D., Ward, W., and Martin, J. H., 2009. Recognizing entailment in intelligent tutoring systems. Natural Language Engineering 15 (04): 479–501.CrossRefGoogle Scholar
Slonim, N., Ran, L., Yonatan, B., Daniel, H., and Ehud, A. 2014. Context dependent claim detection. In COLING 2014.Google Scholar
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.Google Scholar
Stern, A., and Dagan, I. 2013. The biutee research platform for transformation-based textual entailment recognition. Linguistic Issues in Language Technology 9.Google Scholar
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): 203–217.CrossRefGoogle Scholar
Wang, R., and Neumann, G. 2008. An divide-and-conquer strategy for recognizing textual entailment. In Proceedings of the Text Analysis Conference, Gaithersburg, MD.Google Scholar
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.Google Scholar