https://github.com/kudkudak/word-embeddings-benchmarks https://github.com/kudkudak/word-embeddings-benchmarks has a pretty nice evaluation of existing embedding

https://github.com/kudkudak/word-embeddings-benchmarks https://github.com/kudkudak/word-embeddings-benchmarks has a pretty nice evaluation of existing embedding methods. Notably missing from this article is GloVe ( https://nlp.stanford.edu/projects/glove/ https://nlp.stanford.edu/projects/glove/) and LexVec ( https://github.com/alexandres/lexvec https://github.com/alexandres/lexvec ) both which tend to outperform word2vec in both intrinsic and extrinsic tasks. Also of interest are methods which perform retrofitting, improving already trained embeddings. Morph fitting (ACL 2017) is a good example. Hashimoto et al (2016) sheds some interesting insight on how embeddings methods are performing metric recovery. Lots of exciting stuff in this area.