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.
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2K
Citations
180
Authors
102
Institutions
Publications per year
2017–2026
38
Parents
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Making Sense of Word Embeddings
Maria Pelevina, Nikolay Arefiev, Chris Biemann et al. · 2016 · 137 citations · Full text
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Skolkovo Institute of Science and Technology, Russia
2 publications · 151 citations
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102

Chinese Academy of Sciences
Beijing, China
3 h-index
16 publications
2.2K citations

Beijing Institute of Technology
Beijing, China
2 h-index
3 publications
150 citations
Georgia Institute of Technology
Atlanta, United States
2 h-index
3 publications
176 citations

Harbin Institute of Technology
Harbin, China
2 h-index
6 publications
120 citations

Technische Universität Darmstadt
Darmstadt, Germany
2 h-index
5 publications
437 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
4 publications
100 citations
Institute of Automation
Beijing, China
2 h-index
11 publications
2.1K citations
Indiana University Bloomington
Bloomington, United States
2 h-index
3 publications
102 citations
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2 publications · 70 citations
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1 publications · 179 citations
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1 publications · 21 citations
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JCIM · 1 publications · 28 citations
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1 publications · 40 citations
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