Authors
Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
Publication date
2013/1/16
Journal
arXiv preprint arXiv:1301.3781
Description
Abstract: 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, ie it takes less than a day to
learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show ...
Total citations
201320142015201629269937740
Scholar articles
T Mikolov, K Chen, G Corrado, J Dean - arXiv preprint arXiv:1301.3781, 2013