Abstract
Recognizing textual entailment (RTE)—deciding whether one piece of text contains new information with respect to another piece of text—remains a big challenge in natural language processing. One attempt to deal with this problem is combining deep semantic analysis and logical inference, as is done in the Nutcracker RTE system. In doing so, various obstacles will be met on the way: robust semantic analysis, designing interfaces to state-of-the-art theorem provers, and acquiring relevant background knowledge. The coverage of the parser and semantic analysis component is high, yet performance on RTE examples yields high precision but low recall. An empirical study of Nutcracker’s output reveals that the true positives are caused by sophisticated linguistic analysis such as coordination, active-passive alternation, pronoun resolution and relative clauses; the small set of false positives are caused by insufficient syntactic and semantic analyses. But most importantly, the false negatives are produced mainly by lack of background knowledge that is only implicit in the RTE examples.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The source code of the system can be downloaded via the website of the C&C tools Curran et al. (2007).
References
Balduccini, M., Baral, C., & Lierler, Y. (2008). Knowledge representation and question answering. In V. Lifschitz, F. van Harmelen, & B. Porter (Eds.), Handbook of knowledge representation (pp. 779–819). Amsterdam: Elsevier.
Blackburn, P., & Bos, J. (2005). Representation and inference for natural language. A first course in computational semantics. Stanford: CSLI.
Bos, J. (2004). Computational semantics in discourse: Underspecification, resolution, and inference. Journal of Logic, Language and Information, 13(2), 139–157.
Bos, J. (2008). Wide-coverage semantic analysis with Boxer. In J. Bos & R. Delmonte (Eds.), Research in computational semantics: Vol. 1. Semantics in text processing. STEP 2008 conference proceedings (pp. 277–286). London: College Publications.
Bos, J., & Markert, K. (2005). Recognising textual entailment with logical inference. In Proceedings of the 2005 conference on empirical methods in natural language processing (pp. 628–635).
Bos, J., & Nissim, M. (2006). An empirical approach to the interpretation of superlatives. In Proceedings of the 2006 conference on empirical methods in natural language processing, Sydney, Australia (pp. 9–17).
Chierchia, G., & McConnell-Ginet, S. (1991). Meaning and grammar. An introduction to semantics. Cambridge: MIT Press.
Claessen, K., & Sörensson, N. (2003). New techniques that improve mace-style model finding. In P. Baumgartner & C. Fermüller (Eds.), Model computation—principles, algorithms, applications (Cade-19 Workshop), Miami, Florida, USA (pp. 11–27).
Clark, S., & Curran, J. R. (2004). Parsing the WSJ using CCG and log-linear models. In Proceedings of the 42nd annual meeting of the association for computational linguistics (ACL ’04), Barcelona, Spain (pp. 104–111).
Cooper, R., Crouch, D., Van Eijck, J., Fox, C., Van Genabith, J., Jaspars, J., Kamp, H., Pinkal, M., Milward, D., Poesio, M., & Pulman, S. (1996). Using the framework (Technical report). FraCaS: A framework for computational semantics. FraCaS deliverable D16.
Curran, J., Clark, S., & Bos, J. (2007). Linguistically motivated large-scale NLP with C&C and Boxer. In Proceedings of the 45th annual meeting of the association for computational linguistics companion volume proceedings of the demo and poster sessions, Prague, Czech Republic (pp. 33–36).
Dagan, I., Glickman, O., & Magnini, B. (2006). The Pascal recognising textual entailment challenge. In Lecture notes in computer science (Vol. 3944, pp. 177–190).
Gamut, L. (1991). Logic, language, and meaning. Volume II. Intensional logic and logical grammar. Chicago: University of Chicago Press.
Heim, I., & Kratzer, A. (1998). Semantics in generative grammar. Oxford: Blackwell Sci.
Honnibal, M., Curran, J. R., & Bos, J. (2010). Rebanking ccgbank for improved np interpretation. In Proceedings of the 48th meeting of the association for computational linguistics (ACL 2010), Uppsala, Sweden (pp. 207–215).
Kamp, H., & Reyle, U. (1993). From discourse to logic; An introduction to modeltheoretic semantics of natural language, formal logic and DRT. Dordrecht: Kluwer Academic.
Lin, D., & Pantel, P. (2001). DIRT—discovery of inference rules from text. In Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining (pp. 323–328).
Minnen, G., Carroll, J., & Pearce, D. (2001). Applied morphological processing of English. Journal of Natural Language Engineering, 7(3), 207–223.
Monz, C., & de Rijke, M. (2001). Light-weight entailment checking for computational semantics. In P. Blackburn & M. Kohlhase (Eds.), Workshop proceedings ICoS-3 (pp. 59–72).
Muskens, R. (1996). Combining Montague semantics and discourse representation. Linguistics and Philosophy, 19, 143–186.
Riazanov, A., & Voronkov, A. (2002). The design and implementation of vampire. AI Communications, 15(2–3), 91–110.
Sutcliffe, G., & Suttner, C. (2006). The state of CASC. AI Communications, 19(1), 35–48.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Bos, J. (2014). Recognizing Textual Entailment and Computational Semantics. In: Bunt, H., Bos, J., Pulman, S. (eds) Computing Meaning. Text, Speech and Language Technology, vol 47. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7284-7_6
Download citation
DOI: https://doi.org/10.1007/978-94-007-7284-7_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-7283-0
Online ISBN: 978-94-007-7284-7
eBook Packages: Computer ScienceComputer Science (R0)