@InProceedings{fadaee-bisazza-monz:2017:Short1,
  author    = {Fadaee, Marzieh  and  Bisazza, Arianna  and  Monz, Christof},
  title     = {Learning Topic-Sensitive Word Representations},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {441--447},
  abstract  = {Distributed word representations are widely used for modeling words in NLP
	tasks. Most of the existing models generate one representation per word and do
	not consider different meanings of a word.   
	We present two approaches to learn multiple topic-sensitive representations per
	word by using Hierarchical Dirichlet Process. We observe that by modeling
	topics and integrating topic distributions for each document  we obtain
	representations that are able to distinguish between different meanings of a
	given word.
	Our models yield statistically significant improvements for the lexical
	substitution task 
	indicating that commonly used single word representations, even when combined
	with contextual information, are insufficient for this task.},
  url       = {http://aclweb.org/anthology/P17-2070}
}