Journal of Computing Science and Engineering

  • 제5권4호
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  • Pages.288-293
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  • 2011
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  • 1976-4677(pISSN)
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  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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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)
  • 투고 : 2011.06.17
  • 심사 : 2011.11.08
  • 발행 : 2011.12.30
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초록

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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참고문헌

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  4. Hierarchical speech-act classification for discourse analysis vol.34, pp.10, 2013, https://doi.org/10.1016/j.patrec.2013.03.008
  5. Improving domain action classification in goal-oriented dialogues using a mutual retraining method vol.45, 2014, https://doi.org/10.1016/j.patrec.2014.03.021
  6. An Integrated Neural Network Model for Domain Action Determination in Goal-Oriented Dialogues vol.9, pp.2, 2013, https://doi.org/10.3745/JIPS.2013.9.2.259
  7. Post-error Correction in Automatic Speech Recognition Using Discourse Information vol.14, pp.2, 2014, https://doi.org/10.4316/AECE.2014.02009
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