Graph-based Text Similarity

Graph-Based Text Similarity is a text similarity measurement approach that represents textual elements and their relationships as graph structures, where nodes denote linguistic units and edges capture semantic or syntactic dependencies, distinguishing it from vector-space or sequence-based models by explicitly modeling structural and relational information.

150

Publications

14K

Citations

416

Authors

188

Institutions

Publications per year

2017–2026

38

Authors

416

  1. EM
    Einat Minkov

    University of Haifa, Israel

    5 publications · 259 citations

    5

    h-index

  2. WW
    William W. Cohen

    Carnegie Mellon University, United States

    5 publications · 259 citations

    5

    h-index

  3. RM
    Rada Mihalcea

    University of Michigan, United States

    4 publications · 3.9K citations

    4

    h-index

  4. NS
    Naomie Salim

    University of Technology Malaysia, Malaysia

    3 publications · 59 citations

    3

    h-index

  5. DR
    Dragomir Radev

    University of Michigan, United States

    3 publications · 287 citations

    3

    h-index

1–5 of 416

Venues

  1. International Conference on Semantic Computing (ICSC 2007)

    Conference · 3 publications · 379 citations

    3

    h-index

  2. IEEE Access

    3 publications · 133 citations

    3

    h-index

  3. Empirical Methods in Natural Language Processing

    Conference · 2 publications · 3.5K citations

    2

    h-index

  4. 2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 222 citations

    2

    h-index

1–5