Exploring Features and Classifiers for Dialogue Act Segmentation

Akker op den, Harm and Schulz, Christian (2008) Exploring Features and Classifiers for Dialogue Act Segmentation. In: 5th International Workshop on Machine Learning for Multimodal Interaction, MLMI 2008, September 8-10, 2008, Utrecht, The Netherlands.

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Abstract:	This paper takes a classical machine learning approach to the task of Dialogue Act segmentation. A thorough empirical evaluation of features, both used in other studies as well as new ones, is performed. An explorative study to the effectiveness of different classification methods is done by looking at 29 different classifiers implemented in WEKA. The output of the developed classifier is examined closely and points of possible improvement are given.
Item Type:	Conference or Workshop Item
Copyright:	© 2008 Springer
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Human Media Interaction (HMI)
Link to this item:	http://purl.utwente.nl/publications/65340
Official URL:	http://dx.doi.org/10.1007/978-3-540-85853-9_18
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