Extracting descriptive noun phrases from conversational speech

Information extraction on conversational speech presents a number of challenges. The casual speaking style and lower “fact density” typical of spontaneous conversations make named entity extraction less effective than in other domains and leads us to consider extraction of descriptive noun phrases as a more powerful generalization of named entities. We describe a system for finding semantically important noun phrases that combines our conversational speech recognizer with a probabilistic model of syntax and semantics. We present preliminary experiments on a subset of the Switchboard corpus.