Attention, intentions, and the structure of discourse
Computational Linguistics
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Empirical studies on the disambiguation of cue phrases
Computational Linguistics
Backoff model training using partially observed data: application to dialog act tagging
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Investigating the portability of corpus-derived cue phrases for dialogue act classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Error analysis of dialogue act classification
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Influence and power in group interactions
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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In this paper, we present an investigation into the use of cue phrases as a basis for dialogue act classification. We define what we mean by cue phrases, and describe how we extract them from a manually labelled corpus of dialogue. We describe one method of evaluating the usefulness of such cue phrases, by applying them directly as a classifier to unseen utterances. Once we have extracted cue phrases from one corpus, we determine if these phrases are general in nature, by applying them directly as a classification mechanism to a different corpus to that from which they were extracted. Finally, we experiment with increasingly restrictive methods for selecting cue phrases, and demonstrate that there are a small number of core cue phrases that are useful for dialogue act classification.