Paper: Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification

ACL ID N10-1108
Title Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

This paper addresses the problem of learning phrase patterns for unsupervised speaker role classification. Phrase patterns are automati- cally extracted from large corpora, and redun- dant patterns are removed via a graph prun- ing algorithm. In experiments on English and Mandarin talk shows, the use of phrase pat- terns results in an increase of role classifi- cation accuracy over n-gram lexical features, and more compact phrase pattern lists are ob- tained due to the redundancy removal.