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
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13.3K
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752
Authors
311
Institutions
Publications per year
2017–2026
75
Parents
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Azadeh Nikfarjam, Abeed Sarker, Karen O’Connor et al. · Journal of the American Medical Informatics Association · 2015 · 563 citations · Full text
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Sapienza University of Rome
Rome, Italy
10 h-index
21 publications
2.3K citations

University of Manchester
Manchester, United Kingdom
6 h-index
9 publications
396 citations
University of Sheffield
Sheffield, United Kingdom
6 h-index
12 publications
971 citations
The University of Tokyo
Tokyo, Japan
6 h-index
10 publications
1.1K citations

Ghent University
Ghent, Belgium
5 h-index
14 publications
541 citations
University of Utah
Salt Lake City, United States
5 h-index
11 publications
497 citations
Microsoft (United States)
Redmond, United States
4 h-index
7 publications
163 citations
Stanford University
Stanford, United States
4 h-index
14 publications
675 citations
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Repository · 12 publications · 305 citations
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9 publications · 292 citations
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6 publications · 430 citations
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JAMIA · 6 publications · 1.2K citations
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Repository · 4 publications · 180 citations
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