Spoken natural language dialog systems: a practical approach
Spoken natural language dialog systems: a practical approach
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Utilizing statistical dialogue act processing in VERBMOBIL
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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Speaker's intention prediction modules can be widely used as a pre-processor for reducing the search space of an automatic speech recognizer. They also can be used as a preprocessor for generating a proper sentence in a dialogue system. We propose a statistical model to predict speakers' intentions by using multi-level features. Using the multi-level features (morpheme-level features, discourse-level features, and domain knowledge-level features), the proposed model predicts speakers' intentions that may be implicated in next utterances. In the experiments, the proposed model showed better performances (about 29% higher accuracies) than the previous model. Based on the experiments, we found that the proposed multi-level features are very effective in speaker's intention prediction.