Word2vec

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In natural language processing, Word2Vec is a technique for obtaining vector representations of words as word embeddings. These vectors capture information about the meaning of a word based on its surrounding words in a piece of text, following the principles of distributional semantics. Once trained, the model can be used to find words with similar meanings or usage, while its embeddings can serve as inputs to systems for search and classification. Word2Vec was developed by Tomáš Mikolov, Kai Chen, Greg Corrado, Ilya Sutskever and Jeff Dean at Google, published in preprints and presented at ICLR in 2013. Its computational efficiency made it practical to learn high-quality word embeddings from very large text corpora and contributed to their widespread adoption in natural language processing.