[arrmn]

Word embeddings are not just useful for text, they can be applied whenever you have relation between "tokens". You can use them to identifying nodes in graphs that belong to the same group[0]. Another, in my opinion, really interesting idea is to apply them to relational databases[1], you can simply ask for similar rows.

It's a interesting article but the author didn't really provide good arguments why I should stop using w2v.

[0] http://www.kdd.org/kdd2017/papers/view/struc2vec-learning-no... [1] https://arxiv.org/abs/1603.07185

[Matumio]

You may be interested in Facebook's recent StarSpace[1] paper, which also shows how this simple "entity embeddings" approach can be used for different tasks.

[1] https://github.com/facebookresearch/StarSpace

[atrudeau]

And manifold learning: https://people.csail.mit.edu/tommi/papers/HasAlvJaa-TACL16.p...