Efficient Estimation of Word Representations in Vector Space

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.