@InProceedings{shwartz-santus-schlechtweg:2017:EACLlong,
  author    = {Shwartz, Vered  and  Santus, Enrico  and  Schlechtweg, Dominik},
  title     = {Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {65--75},
  abstract  = {The fundamental role of hypernymy in NLP has motivated the development of many
	methods for the automatic identification of this relation, most of which rely
	on word distribution. 
	We investigate an extensive number of such unsupervised measures, using several
	distributional semantic models that differ by context type and feature
	weighting. We analyze the performance of the different methods based on their
	linguistic motivation.
	Comparison to the state-of-the-art supervised methods shows that while
	supervised methods generally outperform the unsupervised ones, the former are
	sensitive to the distribution of training instances, hurting their reliability.
	Being based on general linguistic hypotheses and independent from training
	data, unsupervised measures are more robust, and therefore are still useful
	artillery for hypernymy detection.},
  url       = {http://www.aclweb.org/anthology/E17-1007}
}