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Distributed Representations of Words and Phrases …
[ ] 16 Oct 2013 Distributed Representations of Words and Phrasesand their CompositionalityTomas MikolovGoogle SutskeverGoogle ChenGoogle CorradoGoogle DeanGoogle recently introduced continuous Skip-gram model is an efficient method forlearning high-quality Distributed vector Representations that capture a large num-ber of precise syntactic and semantic word relationships. In this paper we presentseveral extensions that improve both the quality of the vectors and the trainingspeed. By subsampling of the frequent Words we obtain significant speedup andalso learn more regular word Representations . We also describe a simple alterna-tive to the hierarchical softmax called negative inherent limitation of word Representations is their indifference to word orderand their inability to represent idiomatic Phrases .
arXiv:1310.4546v1 [cs.CL] 16 Oct 2013 Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov Google Inc. Mountain View
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