The amazing power of word vectors

For today’s post, I’ve drawn material not just from one paper, but from five! The subject matter is ‘word2vec’ – the work of Mikolov et al. at Google on efficient vector representations of words (and what you can do with them). The papers are:

From the first of these papers (‘Efficient estimation…’) we get a description of the Continuous Bag-of-Words and Continuous Skip-gram models for learning word vectors (we’ll talk about what a word vector is in a moment…). From the second paper we get more illustrations of the power of word vectors, some additional information on optimisations for the skip-gram model (hierarchical softmax and negative sampling), and a discussion of applying word vectors to phrases. The third paper (‘Linguistic Regularities…’) describes vector-oriented reasoning based on word vectors and introduces the famous “King – Man + Woman = Queen” example. The last two papers give a more detailed explanation of some of the very concisely expressed ideas in the Milokov papers.

Check out the word2vec implementation on Google Code.

https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

We are publishing pre-trained word vectors for 90 languages, trained on Wikipedia. These are vectors in dimension 300, trained with the default parameters of fastText.

https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md