In this paper we propose an adaptive method of named entity extraction for meeting understanding. This method combines a statistical model trained from broadcast news data with a cache model built online for ambiguous words, computes their global context name class probability from local context name class probabilities, and integrates name lists information from meeting profiles. Such a fusion of supervised and unsupervised learning has shown improved performance of named entity extraction for meeting applications. When evaluated using manual meeting transcripts, the proposed method demonstrates a 26.07% improvement over the baseline model. Its performance is also comparable to that of the statistical model trained from a small annotated meeting corpus. We are currently applying the proposed method to automatic meeting transcripts.
