Count-based Embeddings

Count-Based Embeddings is a word embedding method that derives semantic representations by constructing and factorizing a global word co-occurrence matrix to capture statistical word relationships across a corpus.

35

Publications

5K

Citations

111

Authors

55

Institutions

Publications per year

2017–2026

23

Variants

Count-based Distributional SemanticsCount-based Word Representations

Authors

111

  1. FS
    Feichen Shen

    Mayo Clinic in Florida, United States

    2 publications · 412 citations

    2

    h-index

  2. MR
    Majid Rastegar-Mojarad

    Mayo Clinic in Florida, United States

    2 publications · 412 citations

    2

    h-index

  3. LW
    Liwei Wang

    Peking University, China

    2 publications · 412 citations

    2

    h-index

  4. ML
    Marina L. Gavrilova

    University of Calgary, Canada

    2 publications · 70 citations

    2

    h-index

  5. PK
    Paul Kingsbury

    Mayo Clinic in Arizona, United States

    2 publications · 412 citations

    2

    h-index

1–5 of 111

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 104 citations

    2

    h-index

  2. ORCA Online Research @Cardiff (Cardiff University)

    Repository · 1 publications · 10 citations

    1

    h-index

  3. Multimedia Tools and Applications

    1 publications · 11 citations

    1

    h-index

  4. Cognitive Computation

    1 publications · 56 citations

    1

    h-index

  5. Acta Polytechnica Hungarica

    1 publications · 13 citations

    1

    h-index

1–5