Extracting phrase patterns with minimum redundancy for unsupervised speaker role classification

Authors:
Bin Zhang;Brian Hutchinson;Wei Wu;Mari Ostendorf
Affiliations:
University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA;University of Washington, Seattle, WA
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

This paper addresses the problem of learning phrase patterns for unsupervised speaker role classification. Phrase patterns are automatically extracted from large corpora, and redundant patterns are removed via a graph pruning algorithm. In experiments on English and Mandarin talk shows, the use of phrase patterns results in an increase of role classification accuracy over n-gram lexical features, and more compact phrase pattern lists are obtained due to the redundancy removal.