Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models

Subhradeep Kayal, Zubair Afzal, George Tsatsaronis, Sophia Katrenko, Pascal Coupet, Marius Doornenbal, Michelle Gregory


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.
Anthology ID:
W17-2327
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
216–221
Language:
URL:
https://www.aclweb.org/anthology/W17-2327
DOI:
10.18653/v1/W17-2327
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2327.pdf