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1 papers

2013ICLR 2013 Workshop Track; arXiv preprint arXiv:1301.3781 — https://arxiv.org/abs/1301.3781

Efficient Estimation of Word Representations in Vector Space

Kai Chen, Greg Corrado, Jeffrey Dean, Tomas Mikolov

Finding: The models learned high-quality word vectors from 1.6 billion words in under a day, orders of magnitude faster than earlier neural approaches. Strikingly, the resulting vector space encoded semantic and syntactic relationships as directions: vector arithmetic such as king − man + woman produced a vector closest to queen, showing that regularities in language were captured as consistent geometric offsets.

vector-spaces word-embeddings