Skip-gram Model

The Skip-Gram Model is a predictive neural network architecture for word embedding that learns distributed representations of words by training on the task of predicting context words given a target word within a fixed window in a corpus.

279

Publications

52.4K

Citations

898

Authors

352

Institutions

Publications per year

2017–2026

192

Authors

898

  1. RK
    Retno Kusumaningrum

    Diponegoro University, Indonesia

    4 publications · 288 citations

    4

    h-index

  2. TM
    Tomáš Mikolov

    Meta (Israel), Israel

    3 publications · 28.3K citations

    3

    h-index

  3. JG
    James Glass

    Massachusetts Institute of Technology, United States

    3 publications · 202 citations

    3

    h-index

  4. YD
    Yuxiao Dong

    Tsinghua University, China

    3 publications · 3K citations

    3

    h-index

  5. BG
    Bin Gao

    Newcastle University, United Kingdom

    3 publications · 372 citations

    3

    h-index

1–5 of 898

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 20.2K citations

    20

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 1.4K citations

    8

    h-index

  3. IEEE Access

    6 publications · 297 citations

    6

    h-index

  4. Procedia Computer Science

    6 publications · 392 citations

    5

    h-index

  5. International Conference on Computational Linguistics

    Conference · 3 publications · 184 citations

    3

    h-index

1–5