Latent Semantic Analysis for Text Segmentation

Freddy Y. Y. Choi, Peter Wiemer-Hastings, Johanna Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper describes a method for linear text segmentation that is more accurate or at least as accurate as state-of-the-art methods (Utiyama and Isahara, 2001; Choi, 2000a). Inter-sentence similarity is estimated by latent semantic analysis (LSA). Boundary locations are discovered by divisive clustering. Test results show LSA is a more accurate similarity measure than the cosine metric (van Rijsbergen, 1979).
Original languageEnglish
Title of host publicationProceedings of the Conference on Empirical Methods in Natural Language Processing
Pages109-117
Number of pages9
Publication statusPublished - 2001

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