@InProceedings{ustalov-EtAl:2017:EACLshort,
  author    = {Ustalov, Dmitry  and  Arefyev, Nikolay  and  Biemann, Chris  and  Panchenko, Alexander},
  title     = {Negative Sampling Improves Hypernymy Extraction Based on Projection Learning},
  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     = {543--550},
  abstract  = {We present a new approach to extraction of hypernyms based on projection
	learning and word embeddings. In contrast to classification-based approaches,
	projection-based methods require no candidate hyponym-hypernym pairs. While it
	is natural to use both positive and negative training examples in supervised
	relation extraction, the impact of positive examples on hypernym prediction was
	not studied so far. In this paper, we show that explicit negative examples used
	for regularization of the model significantly improve performance compared to
	the state-of-the-art approach of Fu et al. (2014) on three datasets from
	different languages.},
  url       = {http://www.aclweb.org/anthology/E17-2087}
}