Parsing Conversational Speech Using Enhanced Segmentation
The lack of sentence boundaries and presence of disfluencies pose difficulties for parsing conversational speech. This work investigates the effects of automatically detecting these phenomena on a probabilistic parser's performance. We demonstrate that a state-of-the-art segmenter, relative to a pause-based segmenter, gives more than 45% of the possible error reduction in parser performance, and that presentation of interruption points to the parser improves performance over using sentence boundaries alone.