@InProceedings{komninos-manandhar:2016:COLING,
  author    = {Komninos, Alexandros  and  Manandhar, Suresh},
  title     = {Structured Generative Models of Continuous Features for Word Sense Induction},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3577--3587},
  abstract  = {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.},
  url       = {http://aclweb.org/anthology/C16-1337}
}