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".
[3]: "Exploiting Similarities among Languages for Machine Translation" - page 2 has an intuitive 2D graphical representation http://arxiv.org/pdf/1309.4168.pdf
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-im...
[2]: 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