Papers › Efficient Estimation of Word Representations in Vector Space

Efficient Estimation of Word Representations in Vector Space

16 Jan 2013arXiv:1301.3781archive 2025-07-28

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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dmaloneynygc/skipGram mentioned on GitHub report
gartland98/word2vec_basic mentioned on GitHubpytorch report
gladisor/TextGenerator mentioned on GitHubpytorch report
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jeremycz/word-vectors mentioned on GitHub report
jind11/word2vec-on-wikipedia mentioned on GitHubMIT report
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tree-park/Word2Vec mentioned on GitHubpytorch report
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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.

4ran · honoured contract
1ran · our draft was wrong
4ran · fixture could not drive it
10ran
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Tasks

Word Similarity

Results from the paper archive 2025-07-28

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Methods

Introduced by this paper: CBoW Word2Vec, Skip-gram Word2Vec

CBoW Word2VecSkip-gram Word2Vec

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