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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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Deeplearning4j includes implementations of term frequency–inverse document frequency , deep learning, and Mikolov's word2vec algorithm, [20] doc2vec, and GloVe, reimplemented and optimized in Java. It relies on t-distributed stochastic neighbor embedding (t-SNE) for word-cloud visualizations.
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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.
The largest models, such as Google's Gemini 1.5, presented in February 2024, can have a context window sized up to 1 million (context window of 10 million was also "successfully tested"). [45] Other models with large context windows includes Anthropic's Claude 2.1, with a context window of up to 200k tokens. [ 46 ]
Google Now, an intelligent personal assistant with a voice interface in Google's Android and Apple Inc.'s iOS, as well as Google Chrome web browser on personal computers. [35] Holmes a new AI created by Wipro. [36] Microsoft Cortana, an intelligent personal assistant with a voice interface in Microsoft's various Windows 10 editions. [37]