Hostname: page-component-745bb68f8f-5r2nc Total loading time: 0 Render date: 2025-01-09T12:09:52.006Z Has data issue: false hasContentIssue false
  • English
  • Français

Recognizing textual entailment: Rational, evaluation and approaches

Published online by Cambridge University Press:  17 November 2009

IDO DAGAN ,
BILL DOLAN ,
BERNARDO MAGNINI  and
DAN ROTH
IDO DAGAN
Affiliation:
Department of Computer Science, Bar Ilan University, Ramat Gan, 52900, Israel e-mail: dagan@cs.biu.ac.il
BILL DOLAN
Affiliation:
Natural Language Processing Group, Microsoft Research, One Microsoft Way, Redmond, WA 98005, USA e-mail: billdol@microsoft.com
BERNARDO MAGNINI
Affiliation:
Human Language Technologies Research Unit, Fondazione Bruno Kessler, Via Sommarive 18, 38050 Povo - Trento (Italy) e-mail: magnini@fbk.eu
DAN ROTH
Affiliation:
Department of Computer Science, University of Illinois at Urbana-Champaign, IL, USA e-mail: danr@uiuc.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.

Type
Papers
Information
Natural Language Engineering , Volume 15 , Special Issue 4: Textual Entailment , October 2009 , pp. i - xvii
Copyright
Copyright © Cambridge University Press 2009

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

Bacchus, F. 1990. Representing and Reasoning with Probabilistic Knowledge. Cambridge, MA, USA, MIT Press.Google Scholar
Baker, C. F., Fillmore, C. J., and Lowe, J. B. 1998. The Berkeley FrameNet project. In Proceedings of the COLING-ACL, Montreal, QC, Canada.Google Scholar
Bar-Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., and Szpektor, I. 2006. The second PASCAL Recognising Textual Entailment Challenge. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy.Google Scholar
Bar-Haim, R., Dagan, I., Mirkin, S., Shnarch, E., Szpektor, I., Berant, J., and Greenthal, I. 17 November 2008. Efficient semantic deduction and approximate matching over compact parse forests. In Proceedings of the TAC 2008 Workshop on Textual Entailment, Gaithersburg, Maryland, USA.Google Scholar
Bar-Haim, R., Szpektor, I., and Glickman, O. 2005. Definition and analysis of intermediate entailment levels. In ACL-05 Workshop on Empirical Modeling of Semantic Equivalence and Entailment, University of Michigan, Ann Arbor, Michigan, USA.Google Scholar
Bos, J., and Markert, K. 2006 When logical inference helps determining textual entailment (and when it doesnt). In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy.Google Scholar
Cabrio, E., Kouylekov, M., and Magnini, B. 17 November 2008. Combining specialized entailment engines for RTE-4. In Proceedings of the TAC 2008 Workshop on Textual Entailment, Gaithersburg, MD.Google Scholar
Chambers, N., Cer, D., Grenager, T., Hall, D., Kiddon, C., MacCartney, B., de Marneffe, M.-C., Ramage, D., Yeh, E., and Christopher, D. M. 28–29 June 2007. Learning alignments and leveraging natural logic, In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic.CrossRefGoogle Scholar
Chklovski, T., and Pantel, P. 2004. VerbOcean: Mining the web for fine-grained semantic verb relations. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-04), Barcelona, Spain.Google Scholar
Chierchia, G., and McConnell-Ginet, S. 2001. Meaning and Grammar: An Introduction to Semantics, 2nd ed.Cambridge, MA: MIT Press.Google Scholar
Clark, P., Harrison, P., Thompson, J., Murray, W., Hobbs, J., and Fellbaum, C. 28–29 June 2007. On the role of lexical and world knowledge in RTE3. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic.CrossRefGoogle Scholar
Condoravdi, C., Crouch, D., de Paiva, V., Stolle, R., and Bobrow, D. G. 2003. Entailment, intensionality and text understanding. HLT-NAACL Workshop on Text Meaning, Edmonton, Alberta, Canada.Google Scholar
Dagan, I., Bar-Haim, R., Szpektor, I., Greental, I., and Shnarch, E. 17–23 February 2008. Natural language as the basis for meaning representation and inference. In Proceedings of the 9th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing08), Haifa, Israel.Google Scholar
Dagan, I., and Glickman, O. 2004. Probabilistic textual entailment: generic applied modeling of language variability, In PASCAL Workshop on Learning Methods for Text Understanding and Mining, Grenoble, France.Google Scholar
Dagan, I., Glickman, O., and Magnini, B. 11–13 April 2005.: The PASCAL Recognising Textual Entailment Challenge. In Proceedings of the First PASCAL Challenges Workshop on Recognising Textual Entailment, Southampton, UK.Google Scholar
Dagan, I., Glickman, O., and Magnini, B. 2006. The PASCAL Recognising Textual Entailment Challenge. In Quinonero-Candela, J., Dagan, I., Magnini, B., and d'Alch-Buc, F. (eds.), Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, 2005, Berlin/Heidelberg, pp. 177–90, Lecture Notes in Computer Science, Vol. 3944, pp. 177–90. Springer-Verlag.Google Scholar
de Marneffe, M.-C.Rafferty, A. N., and Manning, C. D. 16–18 June 2008. Finding contradictions in text. In Proceedings of the ACL 2008: HLT, Columbus, OH.Google Scholar
de Salvo Braz, R., Girju, R., Punyakanok, V., Roth, D., and Sammons, M. 11–13 April 2005. An inference model for semantic entailment in natural language. In Proceedings of the First PASCAL Challenges Workshop on Recognising Textual Entailment, Southampton, UK.Google Scholar
Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Garoufi, K. 2007. Towards a better understanding of applied textual entailment. Master Thesis. Saarbrücken, Germany: Saarland University.Google Scholar
Giampiccolo, D., Magnini, B., Dagan, I., and Dolan, B. 28–29 June 2007 The third PASCAL recognising textual entailment challenge. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing}, Prague, Czech Republic.CrossRefGoogle Scholar
Giampiccolo, D., Trang, DHoa, , Bernardo, M., Dagan, I., and Cabrio, E. 17 November 2008. The fourth PASCAL Recognising Textual Entailment Challenge. In Proceedings of the TAC 2008 Workshop on Textual Entailment. Gaithersburg, MD.Google Scholar
Glickman, O., Dagan, I., and Koppel, M. 2006. A lexical alignment model for probabilistic textual entailment. In Quinonero-Candela, J., Dagan, I., Magnini, B., and d'Alch-Buc, F. (eds.), Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Lecture Notes in Computer Science Vol. 3944, pp. 287–98, Springer-Verlag.Google Scholar
Halpern, J. Y. 1990. An analysis of first-order logics of probability. Artificial Intelligence 46: 311–50.CrossRefGoogle Scholar
Harabagiu, S., Hickl, A., and Lacatusu, F. 16–20 July 2006. Negation, contrast, and contradiction in text processing. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06), Boston, MA.Google Scholar
Iftene, A. 2009. Textual Entailment, PhD Thesis. Iasi, Romania: “Al. I. Cuza” University.Google Scholar
Keefe, R., and Smith, P. (ed.) 1997. Vagueness: A Reader. MIT Press.Google Scholar
KipperSchuler, K. Schuler, K. 2005. VerbNet: a broad-coverage, comprehensive verb lexicon. Dissertations available from ProQuest. University of Pennsylvania, Paper AAI3179808.Google Scholar
Kouleykov, M., and Magnini, B. 2005. Tree edit sistance for textual entailment. In Proceedings of RALNP-2005, International Conference on Recent Advances in Natural Language Processing, Borovets, Bulgaria, pp. 271–8.Google Scholar
Kozareva, Z., and Montoyo, A. 2006. MLEnt: the machine learning entailment system of the University of Alicante. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy.Google Scholar
Landis, J. R., and Koch, G. G. 1997. The measurements of observer agreement for categorical data. Biometrics 33: 159–74.CrossRefGoogle Scholar
Lin, D., and Pantel, P. 2001 DIRT – Discovery of Inference Rules from Text. In Proceedings of ACM Conference on Knowledge Discovery and Data Mining (KDD-01), San Francisco, CA.Google Scholar
Monz, C., and de Rijke, M. 2001. Light-Weight entailment checking for computational semantics. In The Third Workshop on Inference in Computational Semantics (ICoS-3), Siena, Italy.Google Scholar
Szpektor, I., Tanev, H., Dagan, I., and Coppola, B. 2004. Scaling Web-based acquisition of entailment relations. In 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain.Google Scholar
Tatu, M., and Moldovan, D. 28–29 June 2007. COGEX at RTE 3. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague: Czech Republic.Google Scholar
Vanderwende, L., and Dolan, W. B. 2006. What syntax can contribute in the entailment task. In Quinonero-Candela, J., Dagan, I., Magnini, B., and d'Alch-Buc, F. (eds.), Evaluating Predictive Uncertainty, Visual Object Classification, and Recognizing Textual Entailment, First Pascal Machine Learning Challenges Workshop, MLCW 2005, Lecture Notes in Computer Science, Vol. 3944, pp. 205–16. Springer-Verlag.CrossRefGoogle Scholar
Vanderwende, L., Menezes, A., and Snow, R. 10 April 2006. Microsoft research at RTE-2: syntactic contributions in the entailment task: an implementation. In Proceedings of the Second PASCAL Challenges Workshop on Recognising Textual Entailment, Venice, Italy.Google Scholar
Voorhees, E. M., and Harman, D. 1999. Overview of the seventh text retrieval conference. In Proceedings of the Seventh Text REtrieval Conference (TREC-7), Gaithersburg, MD.CrossRefGoogle Scholar
Wang, R., and Neumann, G. 17 November 2008. An accuracy-oriented divide-and-conquer strategy. In Proceedings of the TAC 2008 Workshop on Textual Entailment, Gaithersburg, MD.Google Scholar
Zaenen, A., Karttunen, L., and Crouch, R. 2005. Local textual inference: can it be defined or circumscribed? In Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, Michigan.Google Scholar
Zanzotto, F. M., Pennacchiotti, M.Moschitti, A. 28–29 June 2007. Shallow semantic in fast textual entailment rule learners. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, Prague, Czech Republic.CrossRefGoogle Scholar