Recognizing Textual Entailment and Computational Semantics

  • Johan BosEmail author
Part of the Text, Speech and Language Technology book series (TLTB, volume 47)


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.


Relative Clause Logical Inference Thematic Role Categorial Grammar Model Builder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 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. CrossRefGoogle Scholar
  2. Blackburn, P., & Bos, J. (2005). Representation and inference for natural language. A first course in computational semantics. Stanford: CSLI. Google Scholar
  3. Bos, J. (2004). Computational semantics in discourse: Underspecification, resolution, and inference. Journal of Logic, Language and Information, 13(2), 139–157. MathSciNetCrossRefzbMATHGoogle Scholar
  4. 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. CrossRefGoogle Scholar
  5. 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). Google Scholar
  6. 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). CrossRefGoogle Scholar
  7. Chierchia, G., & McConnell-Ginet, S. (1991). Meaning and grammar. An introduction to semantics. Cambridge: MIT Press. Google Scholar
  8. 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). Google Scholar
  9. 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). Google Scholar
  10. 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. Google Scholar
  11. 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). Google Scholar
  12. Dagan, I., Glickman, O., & Magnini, B. (2006). The Pascal recognising textual entailment challenge. In Lecture notes in computer science (Vol. 3944, pp. 177–190). Google Scholar
  13. Gamut, L. (1991). Logic, language, and meaning. Volume II. Intensional logic and logical grammar. Chicago: University of Chicago Press. Google Scholar
  14. Heim, I., & Kratzer, A. (1998). Semantics in generative grammar. Oxford: Blackwell Sci. Google Scholar
  15. 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). Google Scholar
  16. Kamp, H., & Reyle, U. (1993). From discourse to logic; An introduction to modeltheoretic semantics of natural language, formal logic and DRT. Dordrecht: Kluwer Academic. Google Scholar
  17. 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). Google Scholar
  18. Minnen, G., Carroll, J., & Pearce, D. (2001). Applied morphological processing of English. Journal of Natural Language Engineering, 7(3), 207–223. CrossRefGoogle Scholar
  19. 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). Google Scholar
  20. Muskens, R. (1996). Combining Montague semantics and discourse representation. Linguistics and Philosophy, 19, 143–186. CrossRefGoogle Scholar
  21. Riazanov, A., & Voronkov, A. (2002). The design and implementation of vampire. AI Communications, 15(2–3), 91–110. zbMATHGoogle Scholar
  22. Sutcliffe, G., & Suttner, C. (2006). The state of CASC. AI Communications, 19(1), 35–48. MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Center for Language and Cognition (CLCG)University of GroningenGroningenThe Netherlands

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