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Word sense induction (WSI), which addresses polysemy by unsuperviseddiscovery of multiple word senses, resolves ambiguities for downstream NLPtasks and also makes word representations more interpretable. This paperproposes an accurate and efficient graph-based method for WSI that builds aglobal non-negative vector embedding basis (which are interpretable liketopics) and clusters the basis indexes in the ego network of each polysemousword. By adopting distributional inclusion vector embeddings as our basisformation model, we avoid the expensive step of nearest neighbor search thatplagues other graph-based methods without sacrificing the quality of senseclusters. Experiments on three datasets show that our proposed method producessimilar or better sense clusters and embeddings compared with previousstate-of-the-art methods while being significantly more efficient.

Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings