Word Representation Learning
Word Representation Learning is a computational framework within natural language processing that systematically maps linguistic units to dense vector spaces by capturing syntactic and semantic relationships through distributional patterns in large text corpora.
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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
Convolutional Neural Networks for Sentence Classification
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Carnegie Mellon University
Pittsburgh, United States
32 h-index
145 publications
16.9K citations
University of Cambridge
Cambridge, United Kingdom
29 h-index
114 publications
6.7K citations
Stanford University
Stanford, United States
27 h-index
99 publications
123.9K citations

Harbin Institute of Technology
Harbin, China
27 h-index
85 publications
12.9K citations

Chinese Academy of Sciences
Beijing, China
24 h-index
97 publications
21.7K citations
Tsinghua University
Beijing, China
24 h-index
112 publications
9.4K citations
Microsoft (United States)
Redmond, United States
20 h-index
58 publications
19.4K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
20 h-index
59 publications
4.7K citations
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Repository · 214 publications · 68.8K citations
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TACL · 25 publications · 13.1K citations
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Conference · 18 publications · 664 citations
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