Discourse segmentation of multi-party conversation

Authors:
Michel Galley;Kathleen McKeown;Eric Fosler-Lussier;Hongyan Jing
Affiliations:
Columbia University, New York, NY;Columbia University, New York, NY;Columbia University, New York, NY;IBM T.J. Watson Research Center, Yorktown Heights, NY
Venue:
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Year:
2003

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Abstract

We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm combines knowledge about content using a text-based algorithm as a feature and about form using linguistic and acoustic cues about topic shifts extracted from speech. This segmentation algorithm uses automatically induced decision rules to combine the different features. The embedded text-based algorithm builds on lexical cohesion and has performance comparable to state-of-the-art algorithms based on lexical information. A significant error reduction is obtained by combining the two knowledge sources.