Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Language Understanding Using Two-Level Stochastic Models with POS and Semantic Units

  • Conference paper
  • First Online:
Text, Speech and Dialogue (TSD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2166))

Included in the following conference series:

Abstract

Over the last few years, stochastic models have been widely used in the natural language understanding modeling. Almost all of these works are based on the definition of segments of words as basic semantic units for the stochastic semantic models.

In this work, we present a two—level stochastic model approach to the construction of the natural language understanding component of a dialog system in the domain of database queries. This approach will treat this problem in a way similar to the stochastic approach for the detection of syntactic structures (Shallow Parsing or Chunking) in natural language sentences; however, in this case, stochastic semantic language models are based on the detection of some semantic units from the user turns of the dialog. We give the results of the application of this approach to the construction of the understanding component of a dialog system, which answers queries about a railway timetable in Spanish.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. A. Bonafonte, P. Aibar, N. Castell, E. Lleida, J.B. Mari no, E. Sanchis, and M.I. Torres. Desarrollo de un sistema de diálogo oral en dominios restringidos. In Proceedings of I Jornadas en Tecnología del Habla, 2000.

    Google Scholar 

  2. T. Brants. Cascaded Markov Models. In Proceedings of the EACL99, Bergen, Norway, 1999.

    Google Scholar 

  3. P. Clarksond and R. Ronsenfeld. Statistical Language Modeling using the CMU-Cambridge Toolkit. In Proceedings of Eurospeech, Rhodes, Greece, 1997.

    Google Scholar 

  4. S. M. Katz. Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer. IEEE Transactions on Acoustics, Speech and Signal Processing, 35, 1987.

    Google Scholar 

  5. L. Lamel, S. Rosset, J. Gauvain, S. Bennacef, M. Garnier-Rizet, and B. Prouts. The LIMSI ARISE System. Speech Communication, 31(4):339–353, 2000.

    Article  Google Scholar 

  6. E. Levin and R. Pieraccini. Concept-Based Spontaneous Speech Understanding System. In Proceedings of EUROSPEECH’95, pages 555–558, 1995.

    Google Scholar 

  7. W. Minker. Stocastically—Based Semantic. Analysis for ARISE — Automatic Railway Information Systems for Europe. 2(2):127–147, 1999.

    Google Scholar 

  8. F. Pla, A. Molina, and N. Prieto. Tagging and Chunking with Bigrams. In Proceedings of the COLING— 2000, Saarbrücken, Germany, August 2000.

    Google Scholar 

  9. F. Pla, A. Molina, and N. Prieto. An Integrated Statistical Model for Tagging and Chunking Unrestricted Text. In Proceedings of the Text, Speech and Dialogue 2000, Brno, Czech Republic, September 2000.

    Google Scholar 

  10. F. Pla, A. Molina, and N. Prieto. Improving Chunking by means of Lexical-Contextual Information in Statistical Language Models. In Proceedings of 4th CoNLL-2000 and LLL-2000, Lisbon, Portugal, September 2000.

    Google Scholar 

  11. E. Sanchis, E. Segarra, M. Galiano, F. García, and L. Hurtado. Modelización de la Compresión mediante Técnicas de Aprendizaje Automático. In Proceedings of I Jornadas en Tecnología del Habla, 2000.

    Google Scholar 

  12. R. Schwartz, S. Miller, D. Stallard, and J. Makhoul. Language Understanding using hidden understanding models. In ICSLP, pages 997–1000, 1996.

    Google Scholar 

  13. E. Segarra, V. Arranz, N. Castell, I. Galiano, F. García, A. Molina, and E. Sanchis. Representación Semántica de la Tarea. In Internal Report UPV DSIC-II/5/00, 2000.

    Google Scholar 

  14. E. Segarra, E. Sanchis, M. Galiano, F. García, and L. Hurtado. Extracting Semantic Information through Automatic Learning Techniques. In IX Spanish Symposium on Pattern Recognition and Image Analysis-SNRFAI’ 01, 2001.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departament de Sistemes Informàtics i Computació , Universitat Politècnica de València, Camí de Vera s/n, 46020, València, Spain

    F. Pla, A. Molina, E. Sanchis, E. Segarra & F. García

Authors
  1. F. Pla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Sanchis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. E. Segarra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. García
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, University of West Bohemia in Plzeň, Faculty of Applied Sciences, Univerzitní 22, 306-14, Plzeň, Czech Republic

    Václav Matoušek , Pavel Mautner , Roman Mouček  & Karel Taušer , ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pla, F., Molina, A., Sanchis, E., Segarra, E., García, F. (2001). Language Understanding Using Two-Level Stochastic Models with POS and Semantic Units. In: Matoušek, V., Mautner, P., Mouček, R., Taušer, K. (eds) Text, Speech and Dialogue. TSD 2001. Lecture Notes in Computer Science(), vol 2166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44805-5_54

Download citation

  • DOI: https://doi.org/10.1007/3-540-44805-5_54

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42557-1

  • Online ISBN: 978-3-540-44805-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation