Chris Moody wants you to stop using word2vec:

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

`M`

tensor and then factorizing`M`

into 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.

Kevin Feasel

2017-10-18

Data Science