@inproceedings{a7472ab23ae246748f00402c0c3a9b7c,
title = "Broadcast news story segmentation using manifold learning on latent topic distributions",
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.",
author = "Xiaoming Lu and Lei Xie and Leung, {Cheung Chi} and Bin Ma and Haizhou Li",
year = "2013",
language = "英语",
isbn = "9781937284510",
series = "ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "190--195",
booktitle = "Short Papers",
note = "51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 ; Conference date: 04-08-2013 Through 09-08-2013",
}