Broadcast news story segmentation using manifold learning on latent topic distributions

Xiaoming Lu, Lei Xie, Cheung Chi Leung, Bin Ma, Haizhou Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We present an efficient approach for broadcast news story segmentation using a manifold learning algorithm on latent topic distributions. The latent topic distribution estimated by Latent Dirichlet Allocation (LDA) is used to represent each text block. We employ Laplacian Eigenmaps (LE) to project the latent topic distributions into low-dimensional semantic representations while preserving the intrinsic local geometric structure. We evaluate two approaches employing LDA and probabilistic latent semantic analysis (PLSA) distributions respectively. The effects of different amounts of training data and different numbers of latent topics on the two approaches are studied. Experimental results show that our proposed LDA-based approach can outperform the corresponding PLSA-based approach. The proposed approach provides the best performance with the highest F1-measure of 0.7860.

Original languageEnglish
Title of host publicationShort Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages190-195
Number of pages6
ISBN (Print)9781937284510
StatePublished - 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013

Publication series

NameACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume2

Conference

Conference51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Country/TerritoryBulgaria
CitySofia
Period4/08/139/08/13

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