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An Active Learning Approach for Statistical Spoken Language Understanding

  • Fernando García
  • Lluís-F. Hurtado
  • Emilio Sanchis
  • Encarna Segarra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

In general, large amount of segmented and labeled data is needed to estimate statistical language understanding systems. In recent years, different approaches have been proposed to reduce the segmentation and labeling effort by means of unsupervised o semi-supervised learning techniques. We propose an active learning approach to the estimation of statistical language understanding models that involves the transcription, labeling and segmentation of a small amount of data, along with the use of raw data. We use this approach to learn the understanding component of a Spoken Dialog System. Some experiments that show the appropriateness of our approach are also presented.

Keywords

active learning unaligned corpus spoken language understanding spoken dialog systems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fernando García
    • 1
  • Lluís-F. Hurtado
    • 1
  • Emilio Sanchis
    • 1
  • Encarna Segarra
    • 1
  1. 1.Grup d’Enginyeria del Llenguatge Natural i Reconeixement de Formes, Department de Sistemes Informàtics i ComputacióUniversitat Politècnica de ValènciaValènciaSpain

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