Discourse-level Idea Identification

Discourse-Level Idea Identification is a scholarly analytic process within textual and academic discourse analysis that specifically targets the recognition and extraction of overarching contributions—such as methods, models, frameworks, or theories—at the document level, distinguishing it from sentence- or paragraph-level idea detection by focusing on the synthesis of central innovations or claims that define the paper's academic contribution.

20

Publications

707

Citations

50

Authors

27

Institutions

Publications per year

2017–2026

5

Authors

50

  1. SC
    Seng Chee Tan

    Nanyang Technological University, Singapore

    2 publications · 54 citations

    2

    h-index

  2. AV
    Alwyn Vwen Yen Lee

    Nanyang Technological University, Singapore

    2 publications · 54 citations

    2

    h-index

  3. TL
    Ting Liu

    Shanghai Jiao Tong University, China

    1 publications · 10 citations

    1

    h-index

  4. JB
    Jill Burstein

    Educational Testing Service, United States

    1 publications · 192 citations

    1

    h-index

  5. KD
    Kai D. Knight

    University of Southern California, United States

    1 publications · 192 citations

    1

    h-index

1–5 of 50

Venues

  1. IEEE Intelligent Systems

    1 publications · 192 citations

    1

    h-index

  2. 1

    h-index

  3. 1

    h-index

  4. 1

    h-index

  5. Literary and Linguistic Computing

    LLC · 1 publications · 44 citations

    1

    h-index

1–5