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There is rising interest in vector-space word embeddings and their use inNLP, especially given recent methods for their fast estimation at very largescale. Nearly all this work, however, assumes a single vector per word typeignoring polysemy and thus jeopardizing their usefulness for downstream tasks.We present an extension to the Skip-gram model that efficiently learns multipleembeddings per word type. It differs from recent related work by jointlyperforming word sense discrimination and embedding learning, bynon-parametrically estimating the number of senses per word type, and by itsefficiency and scalability. We present new state-of-the-art results in the wordsimilarity in context task and demonstrate its scalability by training with onemachine on a corpus of nearly 1 billion tokens in less than 6 hours.

Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space