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Enriching Word Vectors with Subword Information

Popular models that learn word representations ignore the morphology of words, by assigning a distinct vector to each word. Article proposes a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations.

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