@InProceedings{vyas-carpuat:2017:starSEM,
  author    = {Vyas, Yogarshi  and  Carpuat, Marine},
  title     = {Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection},
  booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {33--43},
  abstract  = {We introduce WHiC, a challenging testbed for detecting hypernymy, an asymmetric
	relation between words. While previous work has focused on detecting hypernymy
	between word types, we ground the meaning of words in specific contexts drawn
	from WordNet examples, and require predictions to be sensitive to changes in
	contexts. WHiC lets us analyze complementary properties of two approaches of
	inducing vector representations of word meaning in context. We show that such
	contextualized word representations also improve detection of a wider range of
	semantic relations in context.},
  url       = {http://www.aclweb.org/anthology/S17-1004}
}