Key Concept Extraction

Key Concept Extraction is a computational linguistic process that identifies and isolates the most salient, domain-specific concepts from a text, distinguishing itself by focusing on significant, recurring ideas that represent the core intellectual contributions within academic discourse.

79

Publications

4.2K

Citations

278

Authors

118

Institutions

Publications per year

2017–2026

24

Authors

278

  1. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    3 publications · 111 citations

    3

    h-index

  2. LM
    Luís Marujo

    Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, Portugal

    2 publications · 76 citations

    2

    h-index

  3. WZ
    Weinan Zhang

    Harbin Institute of Technology, China

    2 publications · 47 citations

    2

    h-index

  4. DN
    Dae‐Hyun Nam

    University of Toronto, Canada

    2 publications · 139 citations

    2

    h-index

  5. CF
    Chaoyou Fu

    University of Chinese Academy of Sciences, China

    2 publications · 87 citations

    2

    h-index

1–5 of 278

Venues

  1. Expert Systems with Applications

    4 publications · 703 citations

    4

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 64 citations

    3

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 135 citations

    2

    h-index

  4. Information Processing & Management

    1 publications · 16 citations

    1

    h-index

  5. Language

    1 publications · 185 citations

    1

    h-index

1–5