@inproceedings{kayal-etal-2017-tagging,
    title = "Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models",
    author = "Kayal, Subhradeep  and
      Afzal, Zubair  and
      Tsatsaronis, George  and
      Katrenko, Sophia  and
      Coupet, Pascal  and
      Doornenbal, Marius  and
      Gregory, Michelle",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 16th {B}io{NLP} Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/W17-2327/",
    doi = "10.18653/v1/W17-2327",
    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."
}