Latent Semantic Analysis

Latent Semantic Analysis is a dimensionality reduction technique in natural language processing that uses singular value decomposition to identify the underlying semantic structure of a text corpus by mapping terms and documents into a reduced vector space where co-occurring patterns are captured through latent dimensions.

1.7K

Publications

143.1K

Citations

4.1K

Authors

1.2K

Institutions

Publications per year

2017–2026

467

Authors

4.1K

  1. TK
    Thomas K. Landauer

    Core Competence, United States

    20 publications · 27.2K citations

    20

    h-index

  2. PW
    Peter W. Foltz

    University of Colorado Boulder, United States

    15 publications · 6K citations

    14

    h-index

  3. AM
    Andrian Marcus

    Wayne State University, United States

    13 publications · 3K citations

    12

    h-index

  4. SD
    Susan Dumais

    Microsoft (United States), United States

    12 publications · 23K citations

    12

    h-index

  5. WK
    Walter Kintsch

    University of Colorado System, United States

    11 publications · 992 citations

    11

    h-index

1–5 of 4.1K

Venues

  1. arXiv (Cornell University)

    Repository · 32 publications · 3.7K citations

    18

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 21 publications · 1.8K citations

    18

    h-index

  3. Expert Systems with Applications

    13 publications · 890 citations

    13

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 14 publications · 2K citations

    13

    h-index

  5. IEEE Access

    13 publications · 1K citations

    12

    h-index

1–5