Efficient nonparametric estimation of multiple embeddings per word in vector space

There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. Nearly all this work, however, assumes a sin-gle vector per word type—ignoring poly-semy and thus jeopardizing their useful-ness for downstream tasks. We present an extension to the Skip-gram model that efficiently learns multiple embeddings per word type. It differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the num-ber of senses per word type, and by its ef-ficiency and scalability. We present new state-of-the-art results in the word similar-ity in context task and demonstrate its scal-ability by training with one machine on a corpus of nearly 1 billion tokens in less than 6 hours.