Exploring Features and Classifiers for Dialogue Act Segmentation

Harm op den Akker, Christian Husodo Schulz

In: Machine Learning for Multimodal Interaction. Machine Learning and Multimodal Interaction (MLMI-08) 5th International Workshop September 8-10 Utrecht Netherlands Seiten 196-207 Lecture Notes in Computer Science (LNCS) 5237/2008 0302-9743 (Print) 1611-3349 (Online) ISBN 978-3-540-85852-2. Springer Berlin / Heidelberg 9/2008.


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.

Weitere Links

MLMI2008_DA_Segmentation_Submitted.pdf (pdf, 127 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence