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Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words. In particular, words which appear in similar contexts are mapped to vectors which are nearby as measured by cosine similarity .
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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
Mikolov obtained his PhD in Computer Science from Brno University of Technology for his work on recurrent neural network-based language models. [1] [2] He is the lead author of the 2013 paper that introduced the Word2vec technique in natural language processing [3] and is an author on the FastText architecture.
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Visual Tools' VT Speller, introduced in 1994, was "designed for developers of applications that support Windows." [ 18 ] [ 19 ] It came with a dictionary but had the ability to build and incorporate use of secondary dictionaries.