Word Embedding Clustering

Word Embedding Clustering is a document clustering method that employs distributed vector representations of words to capture semantic relationships among documents by grouping them based on the similarity of their embedded word-level features.

47

Publications

2K

Citations

180

Authors

102

Institutions

Publications per year

2017–2026

38

Authors

180

  1. MP
    Maria Pelevina

    Lomonosov Moscow State University, Russia

    2 publications · 151 citations

    2

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  2. HK
    Harrison Kim

    University of Illinois Urbana-Champaign, United States

    2 publications · 50 citations

    2

    h-index

  3. HC
    Hongshu Chen

    Beijing Institute of Technology, China

    2 publications · 135 citations

    2

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  4. AP
    Alexander Panchenko

    Skolkovo Institute of Science and Technology, Russia

    2 publications · 151 citations

    2

    h-index

  5. CB
    Chris Biemann

    Technische Universität Darmstadt, Germany

    2 publications · 151 citations

    2

    h-index

1–5 of 180

Venues

  1. Applied Sciences

    2 publications · 70 citations

    2

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  2. Knowledge-Based Systems

    1 publications · 179 citations

    1

    h-index

  3. Journal of Process Control

    1 publications · 21 citations

    1

    h-index

  4. Journal of Chemical Information and Modeling

    JCIM · 1 publications · 28 citations

    1

    h-index

  5. International Journal of Production Research

    1 publications · 40 citations

    1

    h-index

1–5