Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Dialogue act tagging with Transformation-Based Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
dg.o '08 Proceedings of the 2008 international conference on Digital government research
New research on public deliberation and information technology
Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
Information technology and public deliberation: research on improving public input into government
Proceedings of the 11th Annual International Digital Government Research Conference on Public Administration Online: Challenges and Opportunities
Automatic extraction of cue phrases for cross-corpus dialogue act classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A multi-classifier approach to dialogue act classification using function words
Transactions on Computational Collective Intelligence VII
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We are interested in the area of Dialogue Act (da) tagging. Identifying the dialogue acts of utterances is recognised as an important step towards understanding the content and nature of what speakers say. We have built a simple dialogue act classifier based on purely intra-utterance features – principally word n-gram cue phrases. Although such a classifier performs surprisingly well, rivalling scores obtained using far more sophisticated language modelling techniques for the corpus we address, we want to understand further the issues raised by this approach. We have performed an error analysis of the output of our classifier, with a view to casting light both on the system's performance, and on the da classification scheme itself.