Word2vec


Révision datée du 31 décembre 2018 à 15:57 par Pitpitt (discussion | contributions) (Remplacement de texte — « Termes privilégiés » par « Français »)

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Vocabulary Apprentissage profond

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word2vec

word2vec is an algorithm and tool to learn word embeddings by trying to predict the context of words in a document. The resulting word vectors have some interesting properties, for example vector('queen') ~= vector('king') - vector('man') + vector('woman'). Two different objectives can be used to learn these embeddings: The Skip-Gram objective tries to predict a context from on a word, and the CBOW objective tries to predict a word from its context.