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14954 Exploring Features and Classifiers for Dialogue Act Segmentation
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op den Akker, H. and Schulz, C. (2008) Exploring Features and Classifiers for Dialogue Act Segmentation. In: Machine Learning for Multimodal Interaction, MLMI 2008, 19-20 Sept 2008, Utrecht, the Netherlands. pp. 196-207. Lecture Notes in Computer Science 5237. Springer Verlag. ISBN 978-3-540-85852-2

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Official URL: http://dx.doi.org/10.1007/978-3-540-85853-9_18

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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 Paper (Full Paper, Talk)
Research Group:EWI-HMI: Human Media Interaction
Research Program:CTIT-NICE: Natural Interaction in Computer-mediated Environments
Research Project:AMIDA: Augmented Multi-party Interaction with Distance Access
Uncontrolled Keywords:Machine classification, Conversational Analysis
ID Code:14954
Status:Published
Deposited On:25 January 2009
Refereed:Yes
International:Yes
More Information:statisticsmetis

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