  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 single vector per word type
ignoring polysemy and thus jeopardizing their usefulness 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 number of senses per word type, and by its
efficiency and scalability. We present new state-of-the-art results in the word
similarity in context task and demonstrate its scalability by training with one
machine on a corpus of nearly 1 billion tokens in less than 6 hours.
