I'm uncertain whether you mean the algorithm or the output. The question is interesting to both however. The most common method for producing word vectors, ski

I'm uncertain whether you mean the algorithm or the output. The question is interesting to both however. The most common method for producing word vectors, skip-grams with negative sampling, has been shown to be equivalent to implicitly factorizing a word-context matrix[1]. A related algorithm, GloVe, only uses a word-word co-occurrence matrix to achieve a similar result[2]. You can also view the output as an embedding in a high dimensional space (hence the name word vectors) but more surprisingly you can learn a linear mapping between vector spaces of two languages, which lends it immediately useful in translation. From [3]: "Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish". [1]: "Neural Word Embedding as Implicit Matrix Factorization" http://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.pdf http://papers.nips.cc/paper/5477-neural-word-embedding-as-im... [2]: http://nlp.stanford.edu/projects/glove/ http://nlp.stanford.edu/projects/glove/ [3]: "Exploiting Similarities among Languages for Machine Translation" - page 2 has an intuitive 2D graphical representation http://arxiv.org/pdf/1309.4168.pdf http://arxiv.org/pdf/1309.4168.pdf