Learning word embeddings efficiently with noise-contrastive estimation

This work proposes a simple and scalable new approach to learning word embeddings based on training log-bilinear models with noise-contrastive estimation, and achieves results comparable to the best ones reported, using four times less data and more than an order of magnitude less computing time.