@InProceedings{he-EtAl:2017:EMNLP2017,
  author    = {He, Hua  and  Ganjam, Kris  and  Jain, Navendu  and  Lundin, Jessica  and  White, Ryen  and  Lin, Jimmy},
  title     = {An Insight Extraction System on BioMedical Literature with Deep Neural Networks},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2691--2701},
  abstract  = {Mining biomedical text offers an opportunity to automatically discover
	important facts and infer associations among them. As new scientific findings
	appear across a large collection of biomedical publications, our aim is to tap
	into this literature to automate biomedical knowledge extraction and identify
	important insights from them. Towards that goal, we develop a system with novel
	deep neural networks to extract insights on biomedical literature. Evaluation
	shows our system is able to provide insights with competitive accuracy of human
	acceptance and its relation extraction component outperforms previous work.},
  url       = {https://www.aclweb.org/anthology/D17-1285}
}