Text Anomaly Detection

Text Anomaly Detection is a computational methodology within anomaly detection that specifically identifies deviations from expected patterns in textual data by leveraging linguistic, statistical, or semantic features unique to natural language.

216

Publications

9.7K

Citations

761

Authors

301

Institutions

Publications per year

2017–2026

123

Authors

761

  1. CF
    Cécile Favre

    Centre National de la Recherche Scientifique, France

    3 publications · 188 citations

    3

    h-index

  2. RT
    Ryota Tomioka

    The University of Tokyo, Japan

    3 publications · 133 citations

    3

    h-index

  3. TT
    Toshimitsu Takahashi

    Juntendo University, Japan

    3 publications · 133 citations

    3

    h-index

  4. KY
    Kenji Yamanishi

    NEC (Japan), Japan

    3 publications · 133 citations

    3

    h-index

  5. AG
    Adrien Guille

    Université Lumière Lyon 2, France

    3 publications · 188 citations

    3

    h-index

1–5 of 761

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 413 citations

    11

    h-index

  2. Expert Systems with Applications

    4 publications · 311 citations

    4

    h-index

  3. Neurocomputing

    3 publications · 195 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 143 citations

    3

    h-index

  5. IEEE Access

    4 publications · 168 citations

    3

    h-index

1–5