CHAPTER Vector Semantics and Embeddings
embeddings hypothesis by learning representations of the meaning of words, called embeddings, directly from their distributions in texts. These representations are used in every nat-ural language processing application that makes use of meaning, and the static em-beddings we introduce here underlie the more powerful dynamic or contextualized
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