Semantic Anomaly Detection

Semantic Anomaly Detection is a computational method that identifies deviations from expected meaning in linguistic or symbolic data by analyzing contextual coherence within a semantic representation space.

44

Publications

2.3K

Citations

154

Authors

56

Institutions

Publications per year

2017–2026

20

Authors

154

  1. OR
    Orna Raz

    Carnegie Mellon University, United States

    2 publications · 135 citations

    2

    h-index

  2. AJ
    Anthony J. Sanford

    University of Glasgow, United Kingdom

    2 publications · 265 citations

    2

    h-index

  3. MS
    Mary Shaw

    Carnegie Mellon University, United States

    2 publications · 135 citations

    2

    h-index

  4. PK
    Philip Koopman

    Carnegie Mellon University, United States

    2 publications · 135 citations

    2

    h-index

  5. BH
    Brenda Hannon

    University of Toronto, Canada

    2 publications · 102 citations

    2

    h-index

1–5 of 154

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 55 citations

    3

    h-index

  2. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 93 citations

    1

    h-index

  3. Information Systems

    1 publications · 34 citations

    1

    h-index

  4. HAL (Le Centre pour la Communication Scientifique Directe)

    Repository · 1 publications · 10 citations

    1

    h-index

  5. 1

    h-index

1–5