UMIACS Computational Linguistics Colloquium

December 10, 2003, 11:00am, AVW Room 2120

This talk 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.

For the colloquium series schedule, see the UMD Computational <http://umiacs.umd.edu/~resnik/cl_colloquium/> Linguistics Colloquium Series web page at http://umiacs.umd.edu/~resnik/cl_colloquium/. If you are interested in meeting with the speaker, please contact Doug <http://www.glue.umd.edu/~oard/> Oard (oard@umiacs.umd.edu

<mailto:oard@umiacs.umd.edu> ).