Question Classification by Weighted Combination of Lexical, Syntactic and Semantic Features

  • Babak Loni
  • Gijs van Tulder
  • Pascal Wiggers
  • David M. J. Tax
  • Marco Loog
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6836)

Abstract

We developed a learning-based question classifier for question answering systems. A question classifier tries to predict the entity type of the possible answers to a given question written in natural language. We extracted several lexical, syntactic and semantic features and examined their usefulness for question classification. Furthermore we developed a weighting approach to combine features based on their importance. Our result on the well-known trec questions dataset is competitive with the state-of-the-art on this task.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001) Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm
  2. 2.
    Collins, M.: Head-Driven Statistical Models for natural Language Parsing. PhD thesis, University of Pennsylvania (1999)Google Scholar
  3. 3.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)MATHGoogle Scholar
  4. 4.
    Finlayson, M.A.: MIT Java WordNet Interface series 2 (2008)Google Scholar
  5. 5.
    Huang, Z., Thint, M., Celikyilmaz, A.: Investigation of question classifier in question answering. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP 2009), pp. 543–550 (2009)Google Scholar
  6. 6.
    Huang, Z., Thint, M., Qin, Z.: Question classification using head words and their hypernyms. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008), pp. 927–936 (2008)Google Scholar
  7. 7.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceeding of the 41st Annual Meeting for Computational Linguistics (2003)Google Scholar
  8. 8.
    Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26 (1986)Google Scholar
  9. 9.
    Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th International Conference on Computational linguistics, pp. 1–7. Association for Computational Linguistics (2002)Google Scholar
  10. 10.
    Li, X., Roth, D.: Learning question classifiers: The role of semantic information. In: Proc. International Conference on Computational Linguistics (COLING), pp. 556–562 (2004)Google Scholar
  11. 11.
    Pan, Y., Tang, Y., Lin, L., Luo, Y.: Question classification with semantic tree kernel. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008, pp. 837–838. ACM, New York (2008)Google Scholar
  12. 12.
    Quarteroni, S., Manandhar, S.: Designing an interactive open-domain question answering system. Nat. Lang. Eng. 15, 73–95 (2009)CrossRefGoogle Scholar
  13. 13.
    Silva, J., Coheur, L., Mendes, A., Wichert, A.: From symbolic to sub-symbolic information in question classification. Artificial Intelligence Review 35(2), 137–154 (2011)CrossRefGoogle Scholar
  14. 14.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag New York, Inc., New York (1995)CrossRefMATHGoogle Scholar
  15. 15.
    Voorhees, E.M.: Overview of the trec 2001 question answering track. In: Proceedings of the Tenth Text REtrieval Conference (TREC), pp. 42–51 (2001)Google Scholar
  16. 16.
    Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 26–32. ACM, New York (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Babak Loni
    • 1
  • Gijs van Tulder
    • 1
  • Pascal Wiggers
    • 1
  • David M. J. Tax
    • 1
  • Marco Loog
    • 1
  1. 1.Pattern Recognition LaboratoryDelft University of TechnologyDelftThe Netherlands

Personalised recommendations