Stop Using word2vec

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.

Related Posts

Python and R Data Reshaping

John Mount takes us through a couple of data shaping packages: The advantages of data_algebra and cdata are: – The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).– The transform systems can print what a transform is going to […]

Read More

When to Use Different ML Algorithms

Stefan Franczuk explains the different categories of machine learning algorithms available in Talend: Clustering is the task of grouping together a set of objects in such a way, that objects in the same group are more similar to each other than to those in other groups. Clustering is really useful for identify separate groups and […]

Read More

Categories

October 2017
MTWTFSS
« Sep Nov »
 1
2345678
9101112131415
16171819202122
23242526272829
3031