Structured Generative Models of Continuous Features for Word Sense Induction

We propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction. Our approach jointly models global lexical, local lexical and dependency syntactic context. Each context type is associated with a latent variable and the three types of variables share a hierarchical structure. We use skip-gram based word and dependency context embeddings to construct all three types of representations, reducing the total number of parameters to be estimated and enabling better generalization. We describe an EM algorithm to efficiently estimate model parameters and use the Integrated Complete Likelihood criterion to automatically estimate the number of senses. Our model achieves state-of-the-art results on the SemEval-2010 and SemEval-2013 Word Sense Induction datasets.