A Comparative Study of Mixture Models for Automatic Topic Segmentation of Multiparty Dialogues
نویسندگان
چکیده
In this article we address the task of automatic text structuring into linear and nonoverlapping thematic episodes at a coarse level of granularity. In particular, we deal with topic segmentation on multi-party meeting recording transcripts, which pose specific challenges for topic segmentation models. We present a comparative study of two probabilistic mixture models. Based on lexical features, we use these models in parallel in order to generate a low dimensional input representation for topic segmentation. Our experiments demonstrate that in this manner important information is captured from the data through less features.
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