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EMNLP
2008

Bayesian Unsupervised Topic Segmentation

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Bayesian Unsupervised Topic Segmentation
This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and...
Jacob Eisenstein, Regina Barzilay
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where EMNLP
Authors Jacob Eisenstein, Regina Barzilay
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