A Keras implementation of word2vec, specifically the continuous Skip-gram model for computing continuous vector representations of words from very large data sets. The quality of the word vectors is measured in a word similarity task, with word2vec showing a large improvement in accuracy at a much lower computational cost. Further, word2vec performs at state-of-the-art accuracy for measuring syntactic and semantic word similarities.
Mikolov, Tomas, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781 (2013). https://arxiv.org/pdf/1301.3781.pdf
Sneha Singhania
Nigel Fernandez