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The recently introduced continuous Skip-gram model is an efficient method forlearning high-quality distributed vector representations that capture a largenumber of precise syntactic and semantic word relationships. In this paper wepresent several extensions that improve both the quality of the vectors and thetraining speed. By subsampling of the frequent words we obtain significantspeedup and also learn more regular word representations. We also describe asimple alternative to the hierarchical softmax called negative sampling. Aninherent limitation of word representations is their indifference to word orderand their inability to represent idiomatic phrases. For example, the meaningsof "Canada" and "Air" cannot be easily combined to obtain "Air Canada".Motivated by this example, we present a simple method for finding phrases intext, and show that learning good vector representations for millions ofphrases is possible.

Distributed Representations of Words and Phrases and their Compositionality