@InProceedings{stanovsky-gruhl-mendes:2017:EACLlong,
  author    = {Stanovsky, Gabriel  and  Gruhl, Daniel  and  Mendes, Pablo},
  title     = {Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models},
  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     = {142--151},
  abstract  = {Recognizing mentions of Adverse Drug Reactions (ADR) in social media is
	challenging: ADR mentions are context-dependent and include long, varied and
	unconventional descriptions as compared to more formal medical symptom
	terminology. We use the CADEC corpus to train a recurrent neural network (RNN)
	transducer, integrated with knowledge graph embeddings of DBpedia, and show the
	resulting model to be highly accurate (93.4 F1). 
	Furthermore, even when lacking high quality expert annotations, we show that by
	employing an active learning technique and using purpose built annotation
	tools, we can train the RNN to perform well (83.9 F1).},
  url       = {http://www.aclweb.org/anthology/E17-1014}
}