Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Review of Korean Speech Act Classification: Machine Learning Methods

  • Kim, Hark-Soo (Department of Computer and Communications Engineering, Kangwon National University) ;
  • Seon, Choong-Nyoung (Department of Computer Science and Engineering, Sogang University) ;
  • Seo, Jung-Yun (Department of Computer Science and Engineering/Interdisciplinary Program of Integrated Biotechnology, Sogang University)
  • Received : 2011.06.17
  • Accepted : 2011.11.08
  • Published : 2011.12.30
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Abstract

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.

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