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Status: Unchecked ×Keyword: semantic similarity ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Efficient Estimation of Word Representations in Vector Space

   Mikolov, Chen, Corrado and Jeffrey · arXiv (Cornell University) · 2013

   The authors propose two new model designs for learning word vectors from very large data sets, and report better accuracy at much lower computational cost than earlier neural-network techniques.

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   1. UncheckedTwo new word-vector models improved accuracy over earlier neural methods at far lower cost, learning from 1.6 billion words in under a day.“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.”
   2. UncheckedThe paper reports that its word vectors achieve state-of-the-art results on the authors' own test of syntactic and semantic word similarity.“Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.”