Regulatory Text Mining

Regulatory Text Mining is a computational method that systematically extracts, analyzes, and interprets regulatory information from unstructured textual sources by applying natural language processing and machine learning techniques to identify compliance-related patterns, obligations, and changes within the regulatory landscape.

57

Publications

4.6K

Citations

197

Authors

82

Institutions

Publications per year

2017–2026

18

Authors

197

  1. KH
    Kincho H. Law

    Stanford University, United States

    6 publications · 122 citations

    6

    h-index

  2. NE
    Nora El-Gohary

    University of Illinois Urbana-Champaign, United States

    6 publications · 605 citations

    6

    h-index

  3. GT
    Gloria T. Lau

    Stanford University, United States

    5 publications · 104 citations

    5

    h-index

  4. GW
    Gio Wiederhold

    Stanford University, United States

    4 publications · 80 citations

    4

    h-index

  5. JZ
    Jiansong Zhang

    Purdue University West Lafayette, United States

    3 publications · 439 citations

    3

    h-index

1–5 of 197

Venues

  1. SSRN Electronic Journal

    Repository · 4 publications · 106 citations

    4

    h-index

  2. Journal of Computing in Civil Engineering

    3 publications · 532 citations

    3

    h-index

  3. Decision Support Systems

    2 publications · 97 citations

    2

    h-index

  4. Journal of Medical Internet Research

    1 publications · 90 citations

    1

    h-index

  5. Safety Science

    1 publications · 26 citations

    1

    h-index

1–5