lexvec

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/) and 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.

word2vec yields better representations than PMI-SVD. If you want a better explicit PMI matrix factorization, have a look at https://github.com/alexandres/lexvec and the original paper. Explains why SVD performs poorly. If you are looking for word embedding for production use, checkout fasttext, lexvec, glove, or word2vec. Don't use the approach described in this article.

Google Analogy Task results ( detailed results at http://pastebin.com/PF96nMfX ): Semantic accuracy: 63.84 % Syntactic accuracy: 67.00 % Here performance is not great (great would be >80% on semantic and >70% on syntactic). As this task requires nearest neighbor lookups, performance is impacted by vocabulary size. Since models trained using Wikipedia alone usually limit vocabulary to something ~300k words, we can try that to get scores which are comparable to those posted by the GloVe [1] and LexVec [2] papers by only using the first 300k words in the pre-trained vectors, giving the following results: Semantic accuracy: 77.75 % Syntactic accuracy: 72.55 % Impressive stuff! [1] http://nlp.stanford.edu/pubs/glove.pdf - https://github.com/stanfordnlp/GloVe [2] https://arxiv.org/pdf/1606.01283v1 - https://github.com/alexandres/lexvec