What is recorded here?
Introduced computationally efficient CBOW and skip-gram architectures for learning continuous word representations from very large corpora and evaluated syntactic and semantic regularities in the learned space.
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Scientific scope
The record is the January 2013 four-author paper. The word2vec software, negative sampling, phrase extensions and other distributional-embedding traditions require separate attribution.
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People and systems
Tomas Mikolov; Kai Chen; Greg Corrado; Jeffrey Dean
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Documented countries / regions: United States
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English catalogue narrative
Mikolov and colleagues: efficient word-vector estimation
Introduced computationally efficient CBOW and skip-gram architectures for learning continuous word representations from very large corpora and evaluated syntactic and semantic regularities in the learned space.
Scientific scope
The record is the January 2013 four-author paper. The word2vec software, negative sampling, phrase extensions and other distributional-embedding traditions require separate attribution.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Tomas Mikolov; Kai Chen; Greg Corrado; Jeffrey Dean
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.