Efficient Estimation of Word Representations in Vector Space
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, i.e. it takes less than a day to learn high quality...
Paper Details
Title
Efficient Estimation of Word Representations in Vector Space
Published Date
Jan 16, 2013
Journal
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Notes
History