Recognizing complex discourse acts: a tripartite plan-based model of dialogue
Recognizing complex discourse acts: a tripartite plan-based model of dialogue
The nature of statistical learning theory
The nature of statistical learning theory
A pragmatics-based approach to ellipsis resolution
Computational Linguistics
Analysis system of speech acts and discourse structures using maximum entropy model
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Pattern Recognition Letters
Recognition of Dialogue Acts in Multiparty Meetings Using a Switching DBN
IEEE Transactions on Audio, Speech, and Language Processing
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The analysis of a speech act is important for dialogue understanding systems because the speech act of an utterance is closely associated with the user's intention in the utterance. This paper proposes a speech act classification model that effectively uses a two-layer hierarchical structure generated from the adjacency pair information of speech acts. The proposed model has two advantages when adding hierarchical information to speech act classification; the improved accuracy of the speech act classification and the reduced running time in the testing phase. As a result, it achieves higher performance than other models that do not use the hierarchical structure and has faster running time because Support Vector Machine classifiers can efficiently be arranged on the two-layer hierarchical structure.