Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.

Content

Existing Word2Vec Embeddings.
GoogleNews-vectors-negative300.bin
glove.6B.50d.txt
glove.6B.100d.txt
glove.6B.200d.txt
glove.6B.300d.txt

Acknowledgements

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