@InProceedings{sanchez-riedel:2017:EACLshort,
  author    = {Sanchez, Ivan  and  Riedel, Sebastian},
  title     = {How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis},
  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     = {401--407},
  abstract  = {One key property of word embeddings currently under study is their capacity to
	encode hypernymy. Previous works have used supervised models to recover
	hypernymy structures from embeddings. However, the overall results do not
	clearly show how well we can recover such structures. We conduct the first
	dataset-centric analysis that shows how only the Baroni dataset provides
	consistent results. We empirically show that a possible reason for its good
	performance is its alignment to dimensions specific of hypernymy: generality
	and similarity},
  url       = {http://www.aclweb.org/anthology/E17-2064}
}