@InProceedings{quirk-poon:2017:EACLlong,
  author    = {Quirk, Chris  and  Poon, Hoifung},
  title     = {Distant Supervision for Relation Extraction beyond the Sentence Boundary},
  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     = {1171--1182},
  abstract  = {The growing demand for structured knowledge has led to great interest in
	relation extraction, especially in cases with limited supervision. However,
	existing distance supervision approaches only extract relations expressed in
	single sentences. In general, cross-sentence relation extraction is
	under-explored, even in the supervised-learning setting. In this paper, we
	propose the first approach for applying distant supervision to cross- sentence
	relation extraction. At the core of our approach is a graph representation that
	can incorporate both standard dependencies and discourse relations, thus
	providing a unifying way to model relations within and across sentences. We
	extract features from multiple paths in this graph, increasing accuracy and
	robustness when confronted with linguistic variation and analysis error.
	Experiments on an important extraction task for precision medicine show that
	our approach can learn an accurate cross-sentence extractor, using only a small
	existing knowledge base and unlabeled text from biomedical research articles.
	Compared to the existing distant supervision paradigm, our approach extracted
	twice as many relations at similar precision, thus demonstrating the prevalence
	of cross-sentence relations and the promise of our approach.},
  url       = {http://www.aclweb.org/anthology/E17-1110}
}