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 t

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-node-representations-from-structural-identity http://www.kdd.org/kdd2017/papers/view/struc2vec-learning-no... [1] https://arxiv.org/abs/1603.07185 https://arxiv.org/abs/1603.07185