@InProceedings{zarriess-schlangen:2017:EACLshort,
  author    = {Zarrie{\ss}, Sina  and  Schlangen, David},
  title     = {Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {86--91},
  abstract  = {There has recently been a lot of work trying to use images of referents of
	words for improving vector space meaning representations  derived from text. We
	investigate the opposite direction, as it were, trying to improve visual word
	predictors that identify objects in images, by exploiting distributional
	similarity information during training. We show that for certain words (such as
	entry-level nouns or hypernyms), we can indeed learn better referential word
	meanings by taking into account their semantic similarity to other words. For
	other words, there is no or even a detrimental effect, compared to a learning
	setup that presents even semantically related objects as negative instances.},
  url       = {http://www.aclweb.org/anthology/E17-2014}
}