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

Authors

261

  1. MC
    Michael C. Frank

    Stanford University, United States

    4 publications · 206 citations

    4

    h-index

  2. SG
    Sharon Goldwater

    University of Edinburgh, United Kingdom

    4 publications · 206 citations

    4

    h-index

  3. DB
    Doug Beeferman

    Carnegie Mellon University, United States

    3 publications · 790 citations

    3

    h-index

  4. ES
    Eiichiro Sumita

    National Institute of Information and Communications Technology, Japan

    3 publications · 71 citations

    3

    h-index

  5. JB
    Joshua B. Tenenbaum

    Massachusetts Institute of Technology, United States

    3 publications · 194 citations

    3

    h-index

1–5 of 261

Venues

  1. Zhongwen xinxi xuebao

    4 publications · 73 citations

    4

    h-index

  2. Cognition

    4 publications · 403 citations

    4

    h-index

  3. Computational Linguistics

    2 publications · 138 citations

    2

    h-index

  4. 2

    h-index

  5. Machine Learning

    1 publications · 687 citations

    1

    h-index

1–5