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Also worth mentioning the "competitor" of word2vec: Stanford GloVe https://nlp.stanford.edu/projects/glove/

Haven't had the opportunity to measure the difference in quality, and I've mostly used word2vec until now (with vectors I've trained myself after lemmatization and PoS-tagging of a corpus), but the fact that GloVe provides you different trained models from twitter, Wikipedia and so on is pretty nice




Glove is great. Simpler and faster with a very small trade off for quality. Word2vec has an advantage in that you can produce document vectors with only a small change in the network infrastructure. Tf-idf weighted word vector averages will probably be the best you can do using glove.


The GloVe project just happens to have pre-trained models on their official page.

Word2vec do have a Google News corpus model on their official page, but there are many more trained word2vec models in the literature.


thank you for the feedback, I'll definitely give it a shot.




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