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Abstract: . This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based on a technique that incrementally builds an exponential model to extract features that are correlated with the presence of boundaries in labeled training text. The models use two classes of features: topicality features that use adaptive language models in a novel way to detect broad changes of topic, and cue-word features that detect occurrences of specific words, ... (Update)
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BibTeX entry: (Update)
D. Beeferman, A. Berger, and J. Lafferty. Statistical models for text segmentation. Machine Learning, 1999. To appear. http://citeseer.ist.psu.edu/beeferman99statistical.html More
@article{ beeferman99statistical,
author = "Doug Beeferman and Adam Berger and John D. Lafferty",
title = "Statistical Models for Text Segmentation",
journal = "Machine Learning",
volume = "34",
number = "1-3",
pages = "177-210",
year = "1999",
url = "citeseer.ist.psu.edu/beeferman99statistical.html" }
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