@InProceedings{kayal-EtAl:2017:BioNLP17,
  author    = {Kayal, Subhradeep  and  Afzal, Zubair  and  Tsatsaronis, George  and  Katrenko, Sophia  and  Coupet, Pascal  and  Doornenbal, Marius  and  Gregory, Michelle},
  title     = {Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {216--221},
  abstract  = {In this paper we present a solution for tagging funding bodies and grants in
	scientific articles using a combination of trained sequential learning models,
	namely conditional random fields (CRF), hidden markov models (HMM) and maximum
	entropy models (MaxEnt), on a benchmark set created in-house. We apply the
	trained models to address the BioASQ challenge 5c, which is a newly introduced
	task that aims to solve the problem of funding information extraction from
	scientific articles. Results in the dry-run data set of BioASQ task 5c show
	that the suggested approach can achieve a micro-recall of more than 85% in
	tagging both funding bodies and grants.},
  url       = {http://www.aclweb.org/anthology/W17-2327}
}