Classifying Sentences Using Induced Structure

  • Menno van Zaanen
  • Luiz Augusto Pizzato
  • Diego Mollá
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3772)


In this article we will introduce a new approach (and several implementations) to the task of sentence classification, where pre-defined classes are assigned to sentences. This approach concentrates on structural information that is present in the sentences. This information is extracted using machine learning techniques and the patterns found are used to classify the sentences. The approach fits in between the existing machine learning and hand-crafting of regular expressions approaches, and it combines the best of both. The sequential information present in the sentences is used directly, classifiers can be generated automatically and the output and intermediate representations can be investigated and manually optimised if needed.


Regular Expression Grammatical Inference NIST Special Publication Regular Expression Match Sentence Class 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brill, E.: A simple rule-based part-of-speech tagger. In: Proceedings of ANLP1992, third Conference on Applied Natural Language Processing, Trento, Italy, pp. 152–155 (1992)Google Scholar
  2. 2.
    Clément, J., Flajolet, P., Vallée, B.: The analysis of hybrid trie structures. In: Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 531–539. SIAM Press, Philadelphia (1998)Google Scholar
  3. 3.
    Geertzen, J., van Zaanen, M.: Grammatical inference using suffix trees. In: Paliouras, G., Sakakibara, Y. (eds.) ICGI 2004. LNCS (LNAI), vol. 3264, pp. 163–174. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Hachey, B., Grover, C.: Sentence classification experiments for legal text summarisation. In: Proceedings of the 17th Annual Conference on Legal Knowledge and Information Systems, Jurix 2004 (2004)Google Scholar
  5. 5.
    Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the Abbadingo One DFA learning competition and a new evidence-driven state merging algorithm. In: Honavar, V.G., Slutzki, G. (eds.) ICGI 1998. LNCS (LNAI), vol. 1433, pp. 1–12. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING), Taipei, Taiwan, August 24–September 1. Association for Computational Linguistics (ACL), pp. 556–562 (2002)Google Scholar
  7. 7.
    Pizzato, L.: Using a trie-based structure for question analysis. In: Asudeh, A., Paris, C., Wan, S. (eds.) Proceedings of the Australasian Language Technology Workshop, Macquarie University, Sydney, Australia, December 2004, pp. 25–31. ASSTA (2004)Google Scholar
  8. 8.
    Teufel, S., Moens, M.: Argumentative classification of extracted sentences as a first step towards flexible abstracting. In: Mani, I., Maybury, M. (eds.) Advances in automatic text summarization. MIT Press, Cambridge (1999)Google Scholar
  9. 9.
    Proceedings of the Twelfth Text Retrieval Conference (TREC 2003), Gaithersburg, MD, USA, November 18–21. Number 500-255. NIST Special Publication. Department of Commerce. National Institute of Standards and Technology (2003)Google Scholar
  10. 10.
    van Zaanen, M.: Bootstrapping Structure into Language: Alignment-Based Learning. PhD thesis, University of Leeds, Leeds, UK (January 2002)Google Scholar
  11. 11.
    van Zaanen, M.: Theoretical and practical experiences with Alignment-Based Learning. In: Proceedings of the Australasian Language Technology Workshop, Melbourne, Australia, December 2003, pp. 25–32 (2003)Google Scholar
  12. 12.
    van Zaanen, M., Adriaans, P.: Alignment-Based Learning versus EMILE: A comparison. In: Proceedings of the Belgian-Dutch Conference on Artificial Intelligence (BNAIC, Amsterdam, The Netherlands, October 2001, pp. 315–322 (2001)Google Scholar
  13. 13.
    Voorhees, E.M., Buckland, L.P. (eds.): Proceedings of the Eleventh Text REtrieval Conference (TREC 2002), Gaithersburg, MD, USA, November 19–22, Number 500-251. NIST Special Publication, Department of Commerce, National Institute of Standards and Technology (2002)Google Scholar
  14. 14.
    Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Clarke, C., Cormack, G., Callan, J., Hawking, D., Smeaton, A. (eds.) Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 26–32. ACM Press, New York (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Menno van Zaanen
    • 1
  • Luiz Augusto Pizzato
    • 1
  • Diego Mollá
    • 1
  1. 1.Division of Information and Communication Sciences (ICS), Department of ComputingMacquarie UniversityNorth RydeAustralia

Personalised recommendations