Using Dependency Analysis to Improve Question Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 326)

Abstract

Question classification is a first necessary task of automatic question answering systems. Linguistic features play an important role in developing an accurate question classifier. This paper proposes to use typed dependencies which are extracted automatically from dependency parses of questions to improve accuracy of classification. Experiment results show that with only surface typed dependencies, one can improve the accuracy of a discriminative question classifier by over 8.0% on two benchmark datasets.

Keywords

Dependency Analysis Linear Support Vector Machine Question Classification Prepositional Object Head Word 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li, X., Roth, D.: Learning question classifiers: the role of semantic information. Natural Language Engineering 12(3), 229–249 (2006)CrossRefGoogle Scholar
  2. 2.
    Huang, Z., Thint, M., Qin, Z.: Question classification using head words and their hypernyms. In: Proceedings of the 2008 Conference on EMNLP, pp. 927–936 (2008)Google Scholar
  3. 3.
    Li, X., Roth, D.: Learning question classifiers. In: Proceedings of COLING, pp. 556–569 (2002)Google Scholar
  4. 4.
    Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th ACM SIGIR, pp. 26–32 (2003)Google Scholar
  5. 5.
    Hacioglu, K., Ward, W.: Question classification with support vector machines and error correcting codes. In: Proceedings of NAACL/HLT, pp. 28–30 (2003)Google Scholar
  6. 6.
    Krishnan, V., Das, S., Chakrabarti, S.: Enhanced answer type inference from questions using sequential models. In: Proceedings of EMNLP, pp. 315–322 (2005)Google Scholar
  7. 7.
    Minh, N.L., Thanh, N.T., Akira, S.: Subtree mining for question classification problem. In: Proceedings of IJCAI, pp. 1695–1700 (2007)Google Scholar
  8. 8.
    Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. In: Advances in NIPS (2001)Google Scholar
  9. 9.
    Andrew, G., Gao, J.: Scalable training of l 1-regularized log-linear models. In: ICML, pp. 33–40 (2007)Google Scholar
  10. 10.
    Kübler, S., McDonald, R., Nivre, J.: Dependency Parsing. Morgan & Claypool Publishers (2009)Google Scholar
  11. 11.
    Candito, M., Crabbé, B.: Improve generative statistical parsing with semi-supervised word clustering. In: Proceedings of the Eleventh International Conference on Parsing Technologies (IWPT) (2009)Google Scholar
  12. 12.
    Liang, P., Jordan, M.I., Klein, D.: Learning dependency-based compositional semantics. Computational Linguistics 39(2), 389–446 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    de Marneffe, M.-C., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: Proceedings of LREC 2006, Italy, (2006)Google Scholar
  14. 14.
    Voorhees, E.M.: Overview of TREC 2001 question answering track. In: Proceedings of The Tenth Text REtrieval Conference. NIST (2001)Google Scholar
  15. 15.
    Thanh, N.T., Minh, N.L., Akira, S.: Using semi-supervised learning for question classification. Journal of Natural Language Processing 3(1), 112–130 (2008)Google Scholar
  16. 16.
    Klein, D., Manning, C.D.: Accurate unlexicalized parsing. In: Proceedings of ACL, Sapporo, Japan, pp. 423–430 (July 2003)Google Scholar
  17. 17.
    Le-Hong, P., Nguyen, T.M.H., Azim, R.: Vietnamese parsing with an automatically extracted tree-adjoining grammar. In: Proceedings of the IEEE RIVF, HCMC, Vietnam (2012)Google Scholar
  18. 18.
    Hông Phuong, L.ê., Thi Minh Huyên, N., Roussanaly, A., Vinh, H.T.: A hybrid approach to word segmentation of Vietnamese texts. In: Martín-Vide, C., Otto, F., Fernau, H. (eds.) LATA 2008. LNCS, vol. 5196, pp. 240–249. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Tran, D.H., Chu, C.X., Pham, S.B., Nguyen, M.L.: Learning based approaches for Vietnamese question classification using keywords extraction from the web. In: Proceedings of the IJCNLP, Nagoya, Japan, pp. 740–746 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Phuong Le-Hong
    • 1
  • Xuan-Hieu Phan
    • 2
  • Tien-Dung Nguyen
    • 3
  1. 1.University of Science Vietnam National UniversityHanoiVietnam
  2. 2.University of Engineering and TechnologyVietnam National UniversityHanoiVietnam
  3. 3.FPT ResearchFPT Corp.HanoiVietnam

Personalised recommendations