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Computational Semantics and Knowledge Engineering

  • Johan Bos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

Computational semantics is the business of associating meaning representations with natural language expressions (words, phrases, sentences, and texts), and drawing inferences from these meaning representations [1]. It is an area that has recently matured to a state in which we have at our disposal robust, widecoverage systems that are capable of producing formal semantic representations for open-domain texts. One of such system is Boxer, developed by myself over the last four years [2,3].

References

  1. 1.
    Blackburn, P., Bos, J.: Representation and Inference for Natural Language. A First Course in Computational Semantics. CSLI (2005)Google Scholar
  2. 2.
    Bos, J.: Towards wide-coverage semantic interpretation. In: Proceedings of Sixth International Workshop on Computational Semantics IWCS-6, pp. 42–53 (2005)Google Scholar
  3. 3.
    Curran, J., Clark, S., Bos, J.: 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, June 2007, pp. 33–36. Association for Computational Linguistics (2007)Google Scholar
  4. 4.
    Fellbaum, C. (ed.): WordNet. An Electronic Lexical Database. The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  5. 5.
    Kipper, K., Korhonen, A., Ryant, N., Palmer, M.: A large-scale classification of english verbs. Language Resources and Evaluation 42(1), 21–40 (2008)CrossRefGoogle Scholar
  6. 6.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley FrameNet project. In: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics. Proceedings of the Conference, Université de Montréal, Montreal, Quebec, Canada (1998)Google Scholar
  7. 7.
    Kamp, H.: A Theory of Truth and Semantic Representation. In: Groenendijk, J., Janssen, T.M., Stokhof, M. (eds.) Formal Methods in the Study of Language, pp. 277–322. Mathematical Centre, Amsterdam (1981)Google Scholar
  8. 8.
    Kamp, H., Reyle, U.: From Discourse to Logic; An Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and DRT. Kluwer, Dordrecht (1993)Google Scholar
  9. 9.
    Asher, N.: Reference to Abstract Objects in Discourse. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar
  10. 10.
    Van der Sandt, R.: Presupposition Projection as Anaphora Resolution. Journal of Semantics 9, 333–377 (1992)CrossRefGoogle Scholar
  11. 11.
    Steedman, M.: The Syntactic Process. The MIT Press, Cambridge (2001)Google Scholar
  12. 12.
    Bos, J., Markert, K.: Recognising textual entailment with logical inference techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2005) (2005)Google Scholar
  13. 13.
    Lin, D., Pantel, P.: DIRT—discovery of inference rules from text. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 323–328 (2001)Google Scholar
  14. 14.
    Bos, J.: The “La Sapienza” Question Answering System at TREC 2006. In: Voorhees, et al. (eds.) Proceeding of the Fifteenth Text RETrieval Conference, TREC-2006, Gaithersburg, MD (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Johan Bos
    • 1
  1. 1.Department of Computer ScienceUniversity of Rome “La Sapienza”Italy

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