@inproceedings{1d75576acaaf4876ba3e7a37a6a527fd,
title = "Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models",
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.",
author = "Subhradeep Kayal and Zubair Afzal and George Tsatsaronis and Sophia Katrenko and Pascal Coupet and Marius Doornenbal and Michelle Gregory",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computational Linguistics; 16th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2017 ; Conference date: 04-08-2017",
year = "2017",
language = "Ingl{\'e}s",
series = "BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "216--221",
booktitle = "BioNLP 2017 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 16th BioNLP Workshop",
address = "Estados Unidos",
}