@InProceedings{kober-EtAl:2017:SENSE2017,
  author    = {Kober, Thomas  and  Weeds, Julie  and  Wilkie, John  and  Reffin, Jeremy  and  Weir, David},
  title     = {One Representation per Word - Does it make Sense for Composition?},
  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     = {79--90},
  abstract  = {In this paper, we investigate whether an a priori disambiguation of word senses
	is strictly necessary or whether the meaning of a word in context can be
	disambiguated through composition alone. We evaluate the performance of
	off-the-shelf single-vector and multi-sense vector models on a benchmark phrase
	similarity task and a novel task for word-sense discrimination. We find that
	single-sense vector models perform as well or better than multi-sense vector
	models despite arguably less clean elementary representations. Our findings
	furthermore show that simple composition functions such as pointwise addition
	are able to recover sense specific information from a single-sense vector model
	remarkably well.},
  url       = {http://www.aclweb.org/anthology/W17-1910}
}