<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>9278</REFNUM><AUTHORS><AUTHOR>Misra,H.</AUTHOR><AUTHOR>Yvon,F.</AUTHOR><AUTHOR>Jose,J.M.</AUTHOR><AUTHOR>Cappe,O.</AUTHOR></AUTHORS><YEAR>2009</YEAR><TITLE>Text segmentation via topic modeling: An analytical study</TITLE><PLACE_PUBLISHED>The 18th ACM Conference on Information and Knowledge Management</PLACE_PUBLISHED><PUBLISHER>N/A</PUBLISHER><PAGES>1553--1556</PAGES><LABEL>Misra:2009:9278</LABEL><KEYWORDS><KEYWORD>text segmentation</KEYWORD></KEYWORDS<ABSTRACT>In this paper, the task of text segmentation is approached from a topic modeling perspective. We investigate the use of latent Dirichlet allocation (LDA) topic model to segment a text into semantically coherent segments. A major ben- efit of the proposed approach is that along with the seg- ment boundaries, it outputs the topic distribution associated with each segment. This information is of potential use in applications like segment retrieval and discourse analysis. The new approach outperforms a standard baseline method and yields significantly better performance than most of the available unsupervised methods on a benchmark dataset.</ABSTRACT></RECORD></RECORDS></XML>