Quick Overview
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The main goal of this paper is to introduce techniques that can be used for learning high-quality word vectors from huge data sets with billions of words, and with millions of words in the vocabulary.
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It proposes two novel model architectures - CBOW and Skip-Gram , for computing continuous vector representations of words from very large data sets.
Resources