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See also GloVe, wiki2vec, WordRank, sense2vec. A Continuous embedding method.

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It was recently shown that the word vectors capture many linguistic regularities, for example vector operations vector('Paris') - vector('France') + vector('Italy') results in a vector that is very close to vector('Rome'), and vector('king') - vector('man') + vector('woman') is close to vector('queen') [3, 1].

  • Paper/Levy2014: Neural World Embedding as Implicit Matrix Factorization
  • Paper/Levy2014a: Linguistic Regularities in Sparse and Explicit Word Representations

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Paragaraph vectors #

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