@InProceedings{rimell-EtAl:2017:EACLshort,
  author    = {Rimell, Laura  and  Mabona, Amandla  and  Bulat, Luana  and  Kiela, Douwe},
  title     = {Learning to Negate Adjectives with Bilinear Models},
  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     = {71--78},
  abstract  = {We learn a mapping that negates adjectives by predicting an adjective's antonym
	in an arbitrary word embedding model. We show that both linear models and
	neural networks improve on this task when they have access to a vector
	representing the semantic domain of the input word, e.g. a centroid of
	temperature words when predicting the antonym of 'cold'. We introduce a
	continuous class-conditional bilinear neural network which is able to negate
	adjectives with high precision.},
  url       = {http://www.aclweb.org/anthology/E17-2012}
}