By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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:

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