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Story Segmentation and Topic Classification of Broadcast News via a Topic-Based Segmental Model and a Genetic Algorithm

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2 Author(s)
Chung-Hsien Wu ; Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan ; Chia-Hsin Hsieh

This paper presents a two-stage approach to story segmentation and topic classification of broadcast news. The two-stage paradigm adopts a decision tree and a maximum entropy model to identify the potential story boundaries in the broadcast news within a sliding window. The problem for story segmentation is thus transformed to the determination of a boundary position sequence from the potential boundary regions. A genetic algorithm is then applied to determine the chromosome, which corresponds to the final boundary position sequence. A topic-based segmental model is proposed to define the fitness function applied in the genetic algorithm. The syllable- and word-based story segmentation schemes are adopted to evaluate the proposed approach. Experimental results indicate that a miss probability of 0.1587 and a false alarm probability of 0.0859 are achieved for story segmentation on the collected broadcast news corpus. On the TDT-3 Mandarin audio corpus, a miss probability of 0.1232 and a false alarm probability of 0.1298 are achieved. Moreover, an outside classification accuracy of 74.55% is obtained for topic classification on the collected broadcast news, while an inside classification accuracy of 88.82% is achieved on the TDT-2 Mandarin audio corpus.

Published in:

Audio, Speech, and Language Processing, IEEE Transactions on  (Volume:17 ,  Issue: 8 )