@InProceedings{gupta-boleda-pado:2017:starSEM,
  author    = {Gupta, Abhijeet  and  Boleda, Gemma  and  Pad\'{o}, Sebastian},
  title     = {Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible},
  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     = {104--109},
  abstract  = {Word embeddings are supposed to provide easy access to semantic relations such
	as "male of" (man--woman). While this claim has been investigated for
	concepts, little is known about the distributional behavior of relations of
	(Named) Entities. We describe two word embedding-based models that predict
	values for relational attributes of entities, and analyse them. The task is
	challenging, with major performance differences between relations. Contrary to
	many NLP tasks, high difficulty for a relation does not result from low
	frequency, but from (a) one-to-many mappings; and (b) lack of context patterns
	expressing the relation that are easy to pick up by word
	embeddings.},
  url       = {http://www.aclweb.org/anthology/S17-1012}
}