@inproceedings{pado2017distributed,
  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 de- scribe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance dif- ferences between relations. Contrary to many NLP tasks, high difficulty for a re- lation 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.},
  added-at = {2017-06-05T20:34:14.000+0200},
  address = {Vancouver, BC},
  author = {Gupta, Abhijeet and Boleda, Gemma and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2bc57691623b98eacb1164830fc2b6351/sp},
  booktitle = {Proceedings of STARSEM},
  interhash = {ab0b354752f42d9b50b098d5c3a3ca79},
  intrahash = {bc57691623b98eacb1164830fc2b6351},
  keywords = {conference myown},
  timestamp = {2017-09-27T08:20:17.000+0200},
  title = {Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible},
  url = {http://aclweb.org/anthology/S/S17/S17-1012.pdf},
  year = 2017
}