@InProceedings{eichler-EtAl:2017:starSEM,
  author    = {Eichler, Kathrin  and  Xu, Feiyu  and  Uszkoreit, Hans  and  Krause, Sebastian},
  title     = {Generating Pattern-Based Entailment Graphs for Relation Extraction},
  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     = {220--229},
  abstract  = {Relation extraction is the task of recognizing and extracting relations between
	entities or concepts in texts. A common approach is to exploit existing
	knowledge to learn linguistic patterns expressing the target relation and use
	these patterns for extracting new relation mentions. Deriving relation patterns
	automatically usually results in large numbers of candidates, which need to be
	filtered to derive a subset of patterns that reliably extract correct relation
	mentions. We address the pattern selection task by exploiting the knowledge
	represented by entailment graphs, which capture semantic relationships holding
	among the learned pattern candidates. This is motivated by the fact that a
	pattern may not express the target relation explicitly, but still be useful for
	extracting instances for which the relation holds, because its meaning entails
	the meaning of the target relation. We evaluate the usage of both automatically
	generated and gold-standard entailment graphs in a relation extraction scenario
	and present favorable experimental results, exhibiting the benefits of
	structuring and selecting patterns based on entailment graphs.},
  url       = {http://www.aclweb.org/anthology/S17-1026}
}