Broadcast News Story Segmentation Using Manifold Learning on Latent Topic Distributions

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 t-wo 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.