Towards the Rapid Development of a Natural Language Understanding Module

  • Catarina Moreira
  • Ana Cristina Mendes
  • Luísa Coheur
  • Bruno Martins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6895)


When developing a conversational agent, there is often an urgent need to have a prototype available in order to test the application with real users. A Wizard of Oz is a possibility, but sometimes the agent should be simply deployed in the environment where it will be used. Here, the agent should be able to capture as many interactions as possible and to understand how people react to failure. In this paper, we focus on the rapid development of a natural language understanding module by non experts. Our approach follows the learning paradigm and sees the process of understanding natural language as a classification problem. We test our module with a conversational agent that answers questions in the art domain. Moreover, we show how our approach can be used by a natural language interface to a cinema database.


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  1. 1.
    Aho, A.V., Corasick, M.J.: Efficient string matching: an aid to bibliographic search. Communications of the ACM 18, 333–340 (1975)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Allen, J.: Natural language understanding, 2nd edn. Benjamin-Cummings Publishing Co., Inc. (1995)Google Scholar
  3. 3.
    Androutsopoulos, I., Ritchie, G.D., Thanisch, P.: Natural language interfaces to databases–an introduction. Journal of Language Engineering 1(1), 29–81 (1995)Google Scholar
  4. 4.
    Bernsen, N.O., Dybkjær, L.: Domain-Oriented Conversation with H.C. Andersen. In: Proc. of the Workshop on Affective Dialogue Systems, Kloster Irsee, pp. 142–153. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Bhagat, R., Leuski, A., Hovy, E.: Shallow semantic parsing despite little training data. In: Proc. ACL/SIGPARSE 9th Int. Workshop on Parsing Technologies (2005)Google Scholar
  6. 6.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Prentice-Hall, Englewood Cliffs (2006)Google Scholar
  7. 7.
    Kelley, J.F.: An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Office Information Systems (1984)Google Scholar
  8. 8.
    Kopp, S., Gesellensetter, L., Krämer, N.C., Wachsmuth, I.: A conversational agent as museum guide – design and evaluation of a real-world application. In: Panayiotopoulos, T., Gratch, J., Aylett, R.S., Ballin, D., Olivier, P., Rist, T. (eds.) IVA 2005. LNCS (LNAI), vol. 3661, pp. 329–343. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proceedings of the 11th Annual Conference of the International Speech Communication Association, pp. 1836–1839 (2010)Google Scholar
  10. 10.
    Raux, A., Bohus, D., Langner, B., Black, A.W., Eskenazi, M.: Doing research on a deployed spoken dialogue system: One year of let’s go! experience. In: Proceedings of the 7th Annual Conference of the International Speech Communication Association, pp. 65–68 (2006)Google Scholar
  11. 11.
    Weizenbaum, J.: Eliza - a computer program for the study of natural language communication between man and machine. Communications of the ACM 9(1), 36–45 (1966)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Catarina Moreira
    • 1
  • Ana Cristina Mendes
    • 1
  • Luísa Coheur
    • 1
  • Bruno Martins
    • 1
  1. 1.Instituto Superior Técnico, INESC-IDPorto SalvoPortugal

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