Efficient Estimation of Word Representations in Vector Space. Mikolov, T., Chen, K., Corrado, G., & Dean, J.
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Paper Website abstract bibtex
Paper Website abstract bibtex We propose two novel model architectures for computing continuous vector repre-sentations 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 previ-ously 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 perfor-mance on our test set for measuring syntactic and semantic word similarities.
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title = {Efficient Estimation of Word Representations in Vector Space},
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abstract = {We propose two novel model architectures for computing continuous vector repre-sentations 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 previ-ously 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 perfor-mance on our test set for measuring syntactic and semantic word similarities.},
bibtype = {article},
author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey}
}Downloads: 0
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