PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-document Topic Segmentation
Minwoo Jeong and Ivan Titov
In: The 19th ACM International Conference on Information and Knowledge Management (CIKM-10), 26 Oct - 30 Oct 2010, Toronto, Canada.

Abstract

Multiple documents describing the same or closely related sets of events are common and often easy to obtain: for example, consider document clusters on a news aggregator site or multiple reviews of the same product or service. Even though each such document discusses a similar set of topics, they provide alternative views or complimentary information on each of these topics. We argue that revealing hidden relations by jointly segmenting the documents, or, equivalently, predicting links between topically related segments in different documents would help to visualize documents of interest and construct friendlier user interfaces. In this paper, we refer to this problem as multi-document topic segmentation. We propose an unsupervised Bayesian model for the considered problem that models both shared and document-specic topics, and utilizes Dirichlet process priors to determine the eective number of topics. We show that topic segmentation can be inferred eciently using a simple split- merge sampling algorithm. The resulting method outperforms baseline models on four datasets for multi-document topic segmentation.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:7828
Deposited By:Ivan Titov
Deposited On:17 March 2011