1. Predicting and Tagging Dialog-Act Using MDP and SVM
- Author
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Haifeng Wang, Hua Wu, Chengqing Zong, and Keyan Zhou
- Subjects
Structured support vector machine ,Computer science ,business.industry ,Maximum-entropy Markov model ,Speech recognition ,Markov process ,Pattern recognition ,Machine learning ,computer.software_genre ,Speech processing ,Support vector machine ,Relevance vector machine ,Dialog act ,symbols.namesake ,symbols ,Markov decision process ,Artificial intelligence ,business ,computer - Abstract
Dialog-act tagging is one of the hot topics in processing human-human conversation. In this paper, we introduce a novel model to predict and tag the dialog-act, in which Markov decision process (MDP) is utilized to predict the dialog-act sequence instead of using traditional dialog-act based n-gram, and Support Vector Machine (SVM) is employed to classify the dialog-act for each utterance. The predicting result of MDP and the classifying result of SVM are integrated as the final tagging. The experimental results have shown that our approach outperforms the traditional method.
- Published
- 2008
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