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
279
Distributed Representations of Words and Phrases and their Compositionality
Tomáš Mikolov, Ilya Sutskever, Kai Chen et al. · arXiv (Cornell University) · 2013 · 18.1K citations · Full text
Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami · 2017 · 2.2K citations
Shaosheng Cao, Wei Lu, Qiongkai Xu · 2015 · 1.6K citations
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352
Tsinghua University
Beijing, China
9 h-index
24 publications
3.4K citations
Microsoft (United States)
Redmond, United States
8 h-index
18 publications
5.8K citations

Zhejiang University
Hangzhou, China
6 h-index
22 publications
1.8K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
5 h-index
7 publications
362 citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
8 publications
308 citations
Fudan University
Shanghai, China
4 h-index
14 publications
9.4K citations
Diponegoro University
Semarang, Indonesia
4 h-index
12 publications
876 citations

Bar-Ilan University
Ramat Gan, Israel
4 h-index
8 publications
5.6K citations
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Repository · 31 publications · 20.2K citations
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Conference · 8 publications · 1.4K citations
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6 publications · 297 citations
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6 publications · 392 citations
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Conference · 3 publications · 184 citations
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