Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)

Livre Chapitre de livre Shallow Dialogue Processing Using Machine Learning Algorithms (or Not) eng 2004 author Clark Alexander 502030 author Zufferey Sandrine 501649 author Georgescul Maria 552587 author Lalanne Denis author Popescu Andréi 60818 277-290 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. Dewey 410 LNCS 3361, Springer 82 Département de linguistique 39 false 696 TIM/ISSCO Multimodal Interaction and Related Machine Learning Algorithms B. H. and B. S. Berlin Faculté des lettres / Département de linguistique

Title	Published in	Access level	OA Policy	Year	Views	Downloads

Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)	Multimodal Interaction and Related Machine Learning Algorithms			2004	963	0

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Popescu, Andréi