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Book chapter
English

Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)

Published inMultimodal Interaction and Related Machine Learning Algorithms, Editors B. H. and B. S. Berlin, p. 277-290
PublisherLNCS 3361, Springer
Publication date2004
Abstract

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

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Citation (ISO format)
CLARK, Alexander et al. Shallow Dialogue Processing Using Machine Learning Algorithms (or Not). In: Multimodal Interaction and Related Machine Learning Algorithms. [s.l.] : LNCS 3361, Springer, 2004. p. 277–290.
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  • PID : unige:15031
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Creation04/13/2011 9:31:00 PM
First validation04/13/2011 9:31:00 PM
Update time03/14/2023 4:51:07 PM
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