Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Knowledge and Information Systems
Incorporating speaker and discourse features into speech summarization
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Statistical framework for a Spanish spoken dialogue corpus
Speech Communication
Unsupervised modeling of Twitter conversations
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Decision detection using hierarchical graphical models
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Conversational agents in a virtual world
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
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In recent years Dialogue Acts have become a popular means of modelling the communicative intentions of human and machine utterances in many modern dialogue systems. Many of these systems rely heavily on the availability of dialogue corpora that have been annotated with Dialogue Act labels. The manual annotation of dialogue corpora is both tedious and expensive. Consequently, there is a growing interest in unsupervised systems that are capable of automating the annotation process. This paper investigates the use of a Dirichlet Process Mixture Model as a means of clustering dialogue utterances in an unsupervised manner. These clusters can then be analysed in terms of the possible Dialogue Acts that they might represent. The results presented here are from the application of the Dirichlet Process Mixture Model to the Dihana corpus.