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Effective Korean Speech-act Classification Using the Classification Priority Application and a Post-correction Rules
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 1,  2016, pp.80-86
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.1.80
 Title & Authors
Effective Korean Speech-act Classification Using the Classification Priority Application and a Post-correction Rules
Song, Namhoon; Bae, Kyoungman; Ko, Youngjoong;
 
 Abstract
A speech-act is a behavior intended by users in an utterance. Speech-act classification is important in a dialogue system. The machine learning and rule-based methods have mainly been used for speech-act classification. In this paper, we propose a speech-act classification method based on the combination of support vector machine (SVM) and transformation-based learning (TBL). The user's utterance is first classified by SVM that is preferentially applied to categories with a low utterance rate in training data. Next, when an utterance has negative scores throughout the whole of the categories, the utterance is applied to the correction phase by rules. The results from our method were higher performance over the baseline system long with error-reduction.
 Keywords
ensemble;speech-act classifier;support vector machine;transformation-based learning;
 Language
Korean
 Cited by
 References
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