Machine Learning-based Term Extraction

Machine Learning-Based Term Extraction is a computational approach to identifying domain-specific terminology from textual data by leveraging machine learning algorithms that learn termhood patterns from annotated or unannotated corpora, thereby distinguishing it from rule-based or statistical methods through its reliance on trained models for feature representation and classification.

273

Publications

13.3K

Citations

752

Authors

311

Institutions

Publications per year

2017–2026

75

Authors

752

  1. RN
    Roberto Navigli

    Sapienza University of Rome, Italy

    8 publications · 880 citations

    8

    h-index

  2. SF
    Stefano Faralli

    Sapienza University of Rome, Italy

    6 publications · 519 citations

    6

    h-index

  3. EL
    Els Lefever

    Ghent University, Belgium

    5 publications · 161 citations

    5

    h-index

  4. SA
    Sophia Ananiadou

    University of Manchester, United Kingdom

    5 publications · 372 citations

    5

    h-index

  5. VH
    Véronique Hoste

    Ghent University, Belgium

    4 publications · 120 citations

    4

    h-index

1–5 of 752

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 305 citations

    11

    h-index

  2. BMC Bioinformatics

    9 publications · 292 citations

    9

    h-index

  3. 6

    h-index

  4. Journal of the American Medical Informatics Association

    JAMIA · 6 publications · 1.2K citations

    6

    h-index

  5. PubMed

    Repository · 4 publications · 180 citations

    4

    h-index

1–5