Papers › Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
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Code
Syntology Ran 19 of 62 code samples harvested from 21 repositories linked to this paper; 43 have no recorded run. Of those that ran: 4 ran · honoured contract; 1 ran · our draft was wrong; 4 ran · fixture could not drive it; 10 ran with no contract checked.
By repository: community (archive-listed): 61 samples from 21 repositories, 18 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
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Code Syntology ran Syntology
62 samples harvested; 19 ran; 4 honoured the contract we drafted; 43 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
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Methods
Introduced by this paper: CBoW Word2Vec, Skip-gram Word2Vec
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