Unsupervised Text Segmentation
Unsupervised Text Segmentation is a text segmentation approach that partitions a document into coherent segments without relying on labeled training data, distinguishing itself by leveraging intrinsic textual features and statistical regularities to identify boundary points.
92
Publications
4.4K
Citations
241
Authors
124
Institutions
Publications per year
2017–2026
20
Parents
92
Bayesian unsupervised topic segmentation
Jacob Eisenstein, Regina Barzilay · 2008 · 235 citations
Minimum cut model for spoken lecture segmentation
Igor Malioutov, Regina Barzilay · 2006 · 212 citations · Full text
Computing, Computer Science, Natural Language Processing +12
Online EM for unsupervised models
Percy Liang, Dan Klein · 2009 · 210 citations · Full text
1–5 of 92
241
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h-index
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3
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Laboratoire de Sciences Cognitives et Psycholinguistique, France
3 publications · 129 citations
3
h-index
3
h-index
1–5 of 241
124

City University of Hong Kong
Hong Kong, Hong Kong
4 h-index
8 publications
492 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
4 h-index
6 publications
250 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
9 publications
242 citations
University of Arizona
Tucson, United States
3 h-index
5 publications
102 citations

Macquarie University
Sydney, Australia
3 h-index
3 publications
67 citations

Peking University
Beijing, China
3 h-index
8 publications
155 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
6 publications
1.2K citations
Brown University
Providence, United States
3 h-index
5 publications
1.2K citations
1–8 of 124
Conference · 2 publications · 53 citations
2
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Repository · 2 publications · 34 citations
2
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TCDS · 2 publications · 63 citations
2
h-index
TACL · 2 publications · 23 citations
2
h-index
2 publications · 138 citations
2
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1–5