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

Authors

47

  1. GV
    Gordon V. Cormack

    University of Waterloo, Canada

    8 publications · 484 citations

    8

    h-index

  2. MR
    Maura R. Grossman

    University of Waterloo, Canada

    8 publications · 484 citations

    8

    h-index

  3. EK
    Evangelos Kanoulas

    University of Amsterdam, Netherlands

    3 publications · 87 citations

    3

    h-index

  4. JZ
    Jie Zou

    University of Amsterdam, Netherlands

    2 publications · 55 citations

    2

    h-index

  5. DL
    Dan Li

    University of Amsterdam, Netherlands

    1 publications · 32 citations

    1

    h-index

1–5 of 47

Venues

  1. ACM Transactions on Information Systems

    2 publications · 67 citations

    2

    h-index

  2. Computers in Human Behavior

    1 publications · 12 citations

    1

    h-index

  3. Richmond journal of law & technology

    1 publications · 86 citations

    1

    h-index

  4. Cochrane Evidence Synthesis and Methods

    1 publications · 16 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 67 citations

    1

    h-index

1–5