Embedding-based Similarity

Embedding-Based Similarity is a computational method in text similarity analysis that quantifies semantic relatedness between texts by measuring geometric relationships between their vector representations in a continuous latent space.

181

Publications

13.8K

Citations

572

Authors

276

Institutions

Publications per year

2017–2026

137

Variants

Vector-based Similarity

Authors

572

  1. DS
    Didier Schwab

    Laboratoire d'Informatique de Grenoble, France

    3 publications · 96 citations

    3

    h-index

  2. EM
    El Moatez Billah Nagoudi

    University of British Columbia, Canada

    3 publications · 96 citations

    3

    h-index

  3. OB
    Oren Barkan

    Tel Aviv University, Israel

    3 publications · 108 citations

    3

    h-index

  4. RU
    Raquel Urtasun

    University of Toronto, Canada

    3 publications · 2.4K citations

    3

    h-index

  5. ÁE
    Ábel Elekes

    Karlsruhe Institute of Technology, Germany

    3 publications · 57 citations

    3

    h-index

1–5 of 572

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 1.1K citations

    12

    h-index

  2. Knowledge-Based Systems

    4 publications · 302 citations

    4

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 412 citations

    3

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 290 citations

    2

    h-index

  5. 2

    h-index

1–5