Statistical Text Segmentation
Statistical Text Segmentation is a computational method for partitioning raw text into coherent segments or topics based on probabilistic models that identify distributional patterns of words, distinguishing it from rule-based or lexical cohesion approaches by its reliance on statistical inference over linguistic heuristics.
104
Publications
6.7K
Citations
261
Authors
93
Institutions
Publications per year
2017–2026
5
Parents
104
Statistical Models for Text Segmentation
Doug Beeferman, Adam Berger, John Lafferty · Machine Learning · 1999 · 687 citations · Full text
The Second International Chinese Word Segmentation Bakeoff.
Thomas E. Emerson · 2005 · 308 citations
1–5 of 104
261
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National Institute of Information and Communications Technology, Japan
3 publications · 71 citations
3
h-index
Massachusetts Institute of Technology, United States
3 publications · 194 citations
3
h-index
1–5 of 261
93
Carnegie Mellon University
Pittsburgh, United States
5 h-index
10 publications
3.1K citations
Stanford University
Stanford, United States
4 h-index
4 publications
241 citations
Microsoft (United States)
Redmond, United States
4 h-index
6 publications
415 citations

University of Edinburgh
Edinburgh, United Kingdom
4 h-index
5 publications
233 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
5 publications
1.5K citations

City University of Hong Kong
Hong Kong, Hong Kong
3 h-index
3 publications
155 citations
Seattle University
Seattle, United States
3 h-index
5 publications
149 citations
University of California, Berkeley
Berkeley, United States
3 h-index
4 publications
457 citations
1–8 of 93
4 publications · 73 citations
4
h-index
4 publications · 403 citations
4
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2 publications · 138 citations
2
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IJUFKS · 2 publications · 78 citations
2
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1 publications · 687 citations
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1–5