Hypernymy Detection

Hypernymy Detection is a computational task in lexical semantics that identifies hierarchical relationships between terms where one term (the hyponym) denotes a concept subsumed under another (the hypernym), distinguishing it from relatedness or co-hyponymy detection by specifically targeting asymmetric type–subtype conceptual inclusion.

119

Publications

11K

Citations

303

Authors

133

Institutions

Publications per year

2017–2026

44

Variants

Hypernym Detection

Authors

303

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    6 publications · 264 citations

    6

    h-index

  2. JS
    Jiaming Shen

    University of Illinois Urbana-Champaign, United States

    6 publications · 264 citations

    6

    h-index

  3. ES
    Enrico Santus

    Hong Kong Polytechnic University, Hong Kong SAR China

    6 publications · 436 citations

    6

    h-index

  4. AL
    Alessandro Lenci

    University of Pisa, Italy

    5 publications · 377 citations

    5

    h-index

  5. XH
    Xiaofeng He

    East China Normal University, China

    5 publications · 84 citations

    5

    h-index

1–5 of 303

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 285 citations

    9

    h-index

  2. Computational Linguistics

    3 publications · 390 citations

    3

    h-index

  3. Cognitive Science

    2 publications · 146 citations

    2

    h-index

  4. 2

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 100 citations

    2

    h-index

1–5