Representation and learning in information retrieval
Representation and learning in information retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ECDL '97 Proceedings of the First European Conference on Research and Advanced Technology for Digital Libraries
Story Segmentation and Detection of Commercials in Broadcast News Video
ADL '98 Proceedings of the Advances in Digital Libraries Conference
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
Integrating prosodic and lexical cues for automatic topic segmentation
Computational Linguistics
SeLeCT: a lexical cohesion based news story segmentation system
AI Communications - STAIRS 2002
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Multiple change-point audio segmentation and classification using an MDL-based Gaussian model
IEEE Transactions on Audio, Speech, and Language Processing
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
An evolutionary approach to pattern-based time series segmentation
IEEE Transactions on Evolutionary Computation
Offline speaker segmentation using genetic algorithms and mutual information
IEEE Transactions on Evolutionary Computation
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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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.