Broadcast news story segmentation using manifold learning on latent topic distributions

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

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

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.

源语言英语
主期刊名Short Papers
出版商Association for Computational Linguistics (ACL)
190-195
页数6
ISBN(印刷版)9781937284510
出版状态已出版 - 2013
活动51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, 保加利亚
期限: 4 8月 20139 8月 2013

出版系列

姓名ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
2

会议

会议51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
国家/地区保加利亚
Sofia
时期4/08/139/08/13

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