Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Computational Statistics & Data Analysis
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Multi-paragraph segmentation of expository text
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
A statistical model for domain-independent text segmentation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Web-assisted annotation, semantic indexing and search of television and radio news
WWW '05 Proceedings of the 14th international conference on World Wide Web
Using automatic metadata extraction to build a structured syllabus repository
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
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In this paper, we use Barry and Hartigan's Product Partition Models to formulate text segmentation as an optimization problem, which we solve by a fast dynamic programming algorithm. We test the algorithm on Choi's segmentation benchmark and achieve the best segmentation results so far reported in the literature.