TY - CONF T1 - A lexically-driven algorithm for disfluency detection T2 - Proceedings of HLT-NAACL 2004: Short Papers Y1 - 2004 A1 - Snover,Matthew A1 - Dorr, Bonnie J A1 - Schwartz,Richard AB - This paper describes a transformation-based learning approach to disfluency detection in speech transcripts using primarily lexical features. Our method produces comparable results to two other systems that make heavy use of prosodic features, thus demonstrating that reasonable performance can be achieved without extensive prosodic cues. In addition, we show that it is possible to facilitate the identification of less frequently disfluent discourse markers by taking speaker style into account. JA - Proceedings of HLT-NAACL 2004: Short Papers T3 - HLT-NAACL-Short '04 PB - Association for Computational Linguistics CY - Stroudsburg, PA, USA SN - 1-932432-24-8 UR - http://dl.acm.org/citation.cfm?id=1613984.1614024 ER -