Technology-assisted Review
Technology-Assisted Review is a document review methodology that employs computational techniques, such as machine learning and natural language processing, to prioritize, classify, or identify relevant documents in large-scale data sets, thereby enhancing review efficiency and accuracy compared to manual review.
28
Publications
1.2K
Citations
47
Authors
40
Institutions
Publications per year
2017–2026
17
Variants
TarTechnology Assisted Review
Parents
28
1–5 of 28
47
8
h-index
8
h-index
3
h-index
2
h-index
1
h-index
1–5 of 47
40

University of Waterloo
Waterloo, Canada
5 h-index
7 publications
336 citations
Georgetown University
Washington, United States
3 h-index
6 publications
100 citations

University of Amsterdam
Amsterdam, The Netherlands
3 h-index
7 publications
194 citations
Bechtel (United States)
San Francisco, United States
3 h-index
3 publications
284 citations
City College
Fort Lauderdale, United States
1 h-index
1 publications
12 citations

The Open University
Milton Keynes, United Kingdom
1 h-index
2 publications
94 citations

Macquarie University
Sydney, Australia
1 h-index
1 publications
162 citations

University of York
York, United Kingdom
1 h-index
5 publications
80 citations
1–8 of 40
2 publications · 67 citations
2
h-index
1 publications · 12 citations
1
h-index
1 publications · 86 citations
1
h-index
1 publications · 16 citations
1
h-index
Repository · 1 publications · 67 citations
1
h-index
1–5