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

Authors

241

  1. MJ
    Mark Johnson

    Brown University, United States

    6 publications · 608 citations

    7

    h-index

  2. CK
    Chunyu Kit

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 246 citations

    4

    h-index

  3. PR
    Paul R. Cohen

    University of Massachusetts Amherst, United States

    3 publications · 67 citations

    3

    h-index

  4. ED
    Emmanuel Dupoux

    Laboratoire de Sciences Cognitives et Psycholinguistique, France

    3 publications · 129 citations

    3

    h-index

  5. HZ
    Hai Zhao

    Shanghai Jiao Tong University, China

    3 publications · 97 citations

    3

    h-index

1–5 of 241

Venues

  1. International Joint Conference on Artificial Intelligence

    Conference · 2 publications · 53 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 34 citations

    2

    h-index

  3. IEEE Transactions on Cognitive and Developmental Systems

    TCDS · 2 publications · 63 citations

    2

    h-index

  4. 2

    h-index

  5. Computational Linguistics

    2 publications · 138 citations

    2

    h-index

1–5