  Word sense induction (WSI), which addresses polysemy by unsupervised
discovery of multiple word senses, resolves ambiguities for downstream NLP
tasks and also makes word representations more interpretable. This paper
proposes an accurate and efficient graph-based method for WSI that builds a
global non-negative vector embedding basis (which are interpretable like
topics) and clusters the basis indexes in the ego network of each polysemous
word. By adopting distributional inclusion vector embeddings as our basis
formation model, we avoid the expensive step of nearest neighbor search that
plagues other graph-based methods without sacrificing the quality of sense
clusters. Experiments on three datasets show that our proposed method produces
similar or better sense clusters and embeddings compared with previous
state-of-the-art methods while being significantly more efficient.
