Abstract
This paper describes the PASCAL Network of Excellence first Recognising Textual Entailment (RTE-1) Challenge benchmark. The RTE task is defined as recognizing, given two text fragments, whether the meaning of one text can be inferred (entailed) from the other. This application-independent task is suggested as capturing major inferences about the variability of semantic expression which are commonly needed across multiple applications. The Challenge has raised noticeable attention in the research community, attracting 17 submissions from diverse groups, suggesting the generic relevance of the task.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
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)
Chierchia, G., McConnell-Ginet, S.: Meaning and grammar: An introduction to semantics, 2nd edn. MIT Press, Cambridge (2001)
Condoravdi, C., Crouch, D., de Paiva, V., Stolle, R., Bobrow, D.G.: Entailment, intensionality and text understanding. In: HLT-NAACL Workshop on Text Meaning (2003)
Corley, C., Mihalcea, R.: Measuring the Semantic Similarity of Texts. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, Ann Arbor, June 2005, 1318 pages (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, Grenoble, France, January 26- 29 (2004)
Glickman, O., Dagan, I., Koppel, M.: A Lexical Alignment Model for Probabilistic Textual Entailment. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 287–298. Springer, Heidelberg (2006)
Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990)
Keefe, R., Smith, P. (eds.): Vagueness: A Reader. MIT Press, Cambridge (1997)
Lukasiewicz, J.: Selected Works. In: Borkowski, L. (ed.). North Holland, London (1970)
Marsi, E., Krahmer, E.: Classification of Semantic Relations by Humans and Machines. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005)
Monz, C., de Rijke, M.: Light-Weight Entailment Checking for Computational Semantics. In: The third workshop on inference in computational semantics, ICoS-3 (2001)
Vanderwende, L., Dolan, W.B.: What Syntax can Contribute in the Entailment Task. In: Quiñonero-Candela, J., et al. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 205–216. Springer, Heidelberg (2006)
Szpektor, I., Tanev, H., Dagan, I., Coppola, B.: Scaling Web-based Acquisition of Entailment Relations. In: Empirical Methods in Natural Language Processing (EMNLP) (2004)
Zadeh, L.: Fuzzy sets. Information and Control 8 (1965)
Zaenen, A., Karttunen, L., Crouch, R.: Local Textual Inference: Can it be Defined or Circumscribed? In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dagan, I., Glickman, O., Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge. 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_9
Download citation
DOI: https://doi.org/10.1007/11736790_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
Online ISBN: 978-3-540-33428-6
eBook Packages: Computer ScienceComputer Science (R0)