In a different line of work, Vilnis and McCallum (2015) proposed representing words as Gaussian distributions to embed uncertainty in dimensions of the embedding to better capture concepts like entailment.
athiwaratkun and Wilson (2017) argued that such a single prototype model can't capture multiple distinct meanings and proposed W ord2GM to learn multiple Gaussian embeddings per word.