When I started playing with word2vec four years ago I needed (and luckily had) tons of supercomputer time. But because of advances in our understanding of word2vec, computing word vectors now takes fifteen minutes on a single run-of-the-mill computer with standard numerical libraries. Word vectors are awesome but you don’t need a neural network – and definitely don’t need deep learning – to find them. So if you’re using word vectors and aren’t gunning for state of the art or a paper publication then stop using word2vec.
Chris has a follow-up post on word tensors as well:
There’s only three steps to computing word tensors. Counting word-word-document skipgrams, normalizing those counts to form the PMI-like
Mtensor and then factorizing
Minto smaller matrices.
But to actually perform the factorization we’ll need to generalize the SVD to higher rank tensors 1. Unfortunately, tensor algebra libraries aren’t very common 2. We’ve written one for non-negative sparse tensor factorization, but because the PMI can be both positive and negative it isn’t applicable here. Instead, for this application I’d recommend HOSVD as implemented in scikit-tensor. I’ve also heard good things about tensorly.
I’m going to keep using word2vec for now, but it’s a good pair of posts.