Probabilistic latent semantic analysis for broadcast news story segmentation
Mi-Mi Lu, Cheung-Chi Leung, Lei Xie, Bin Ma, Haizhou Li · 2011
This paper proposes to perform probabilistic latent semantic analysis (PLSA) for broadcast news (BN) story segmentation. PLSA exploits a deeper underlying relation among terms be-yond their occurrences thus conceptual matching can be em-ployed to replace literal term matching. Different from text seg-mentation, lexical based BN story segmentation has to be car-ried out over LVCSR transcripts, where the incorrect recogni-tion of out-of-vocabulary words inevitably impacts the seman-tic relation. We use phoneme subwords as the basic term units to address this problem. We integrate a cross entropy mea-surement with PLSA to depict lexical cohesion and compare its performance with the widely used cosine similarity metric. Furthermore, we evaluate two approaches, namely TextTiling and dynamic programming (DP), for story boundary identifica-tion. Experimental results show that the PLSA based methods bring a significant performance boost to story segmentation and the cross entropy based DP approach provides the best perfor-mance. Index Terms: story segmentation, probabilistic latent semantic analysis, cross entropy, dynamic programming, spoken docu-ment retrieval 1.