@InProceedings{cocos-apidianaki-callisonburch:2017:SENSE2017,
  author    = {Cocos, Anne  and  Apidianaki, Marianna  and  Callison-Burch, Chris},
  title     = {Word Sense Filtering Improves Embedding-Based Lexical Substitution},
  booktitle = {Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {110--119},
  abstract  = {The role of word sense disambiguation in lexical substitution has been
	questioned due to the high performance of vector space models which propose
	good substitutes without explicitly accounting for sense. We show that a
	filtering
	mechanism based on a sense inventory optimized for substitutability can improve
	the results of these models. Our sense inventory is constructed using a
	clustering method which generates paraphrase clusters that are congruent with
	lexical substitution annotations in a development set. The results show that
	lexical substitution can still benefit from senses which can improve the output
	of vector space paraphrase ranking models.},
  url       = {http://www.aclweb.org/anthology/W17-1914}
}