Feature-based Similarity

Feature-Based Similarity is a computational method in cognate identification that determines linguistic relatedness by comparing abstract, structurally derived features of words—such as phonological, orthographic, or morphological attributes—rather than surface-level string alignment, thereby enabling systematic discrimination of true cognates from chance resemblances.

281

Publications

14.2K

Citations

1K

Authors

381

Institutions

Publications per year

2017–2026

137

Authors

1K

  1. GK
    Grzegorz Kondrak

    University of Alberta, Canada

    5 publications · 417 citations

    5

    h-index

  2. DK
    Dmitry Korkin

    University of Missouri, United States

    2 publications · 26 citations

    2

    h-index

  3. CS
    Changming Sun

    Commonwealth Scientific and Industrial Research Organisation, Australia

    2 publications · 248 citations

    2

    h-index

  4. FW
    Fang‐Xiang Wu

    University of Saskatchewan, Canada

    2 publications · 119 citations

    2

    h-index

  5. CS
    Chunhua Shen

    The University of Adelaide, Australia

    2 publications · 248 citations

    2

    h-index

1–5 of 1K

Venues

  1. 7

    h-index

  2. IEEE Access

    6 publications · 218 citations

    6

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 354 citations

    6

    h-index

  4. arXiv (Cornell University)

    Repository · 6 publications · 583 citations

    6

    h-index

  5. IEEE Transactions on Multimedia

    5 publications · 335 citations

    5

    h-index

1–5