A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The reliability of a dialogue structure coding scheme
Computational Linguistics
Discourse chunking: a tool in dialogue act tagging
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Modeling dialogue structure with adjacency pair analysis and hidden Markov models
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
The impact of task-oriented feature sets on HMMs for dialogue modeling
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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A problem in dialogue research is that of finding and managing expectations. Adjacency pair theory has widespread acceptance, but traditional classification features (in particular, 'previous-tag' type features) do not exploit this information optimally. We suggest a method of dialogue segmentation that verifies adjacency pairs and allows us to use dialogue-level information within the entire segment and not just the previous utterance. We also use the X2 test for statistical significance as 'noise reduction' to refine a list of pairs. Together, these methods can be used to extend expectation beyond the traditional classification features.