@InProceedings{noriegaatala-EtAl:2017:EMNLP2017,
  author    = {Noriega-Atala, Enrique  and  Valenzuela-Esc\'{a}rcega, Marco A.  and  Morrison, Clayton  and  Surdeanu, Mihai},
  title     = {Learning what to read: Focused machine reading},
  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     = {2905--2910},
  abstract  = {Recent efforts in bioinformatics have achieved tremendous progress in the ma-
	chine reading of biomedical literature, and the assembly of the extracted
	biochem- ical interactions into large-scale models such as protein signaling
	pathways. How- ever, batch machine reading of literature at today’s scale
	(PubMed alone indexes over 1 million papers per year) is unfea- sible due to
	both cost and processing over- head. In this work, we introduce a focused
	reading approach to guide the machine reading of biomedical literature towards
	what literature should be read to answer a biomedical query as efficiently as
	pos- sible. We introduce a family of algorithms for focused reading, including
	an intuitive, strong baseline, and a second approach which uses a reinforcement
	learning (RL) framework that learns when to explore (widen the search) or
	exploit (narrow it). We demonstrate that the RL approach is capable of
	answering more queries than the baseline, while being more efficient, i.e.,
	reading fewer documents.},
  url       = {https://www.aclweb.org/anthology/D17-1313}
}