@InProceedings{kanada-kobayashi-hayashi:2017:SENSE2017,
  author    = {Kanada, Kentaro  and  Kobayashi, Tetsunori  and  Hayashi, Yoshihiko},
  title     = {Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations},
  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     = {37--46},
  abstract  = {This paper proposes a method for classifying the type of lexical-semantic
	relation between a given pair of words. Given an inventory of target
	relationships, this task can be seen as a multi-class classification problem.
	We train a supervised classifier by assuming: (1) a specific type of
	lexical-semantic relation between a pair of words would be indicated by a
	carefully designed set of relation-specific similarities associated with the
	words; and (2) the similarities could be effectively computed by ``sense
	representations'' (sense/concept embeddings). The experimental results show
	that the proposed method clearly outperforms an existing state-of-the-art
	method that does not utilize sense/concept embeddings, thereby demonstrating
	the effectiveness of the sense representations.},
  url       = {http://www.aclweb.org/anthology/W17-1905}
}