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,
- Editors:
- Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 216–221
- Language:
- URL:
- https://aclanthology.org/W17-2327
- DOI:
- 10.18653/v1/W17-2327
- Cite (ACL):
- Subhradeep Kayal, Zubair Afzal, George Tsatsaronis, Sophia Katrenko, Pascal Coupet, Marius Doornenbal, and Michelle Gregory. 2017. Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models. In BioNLP 2017, pages 216–221, Vancouver, Canada,. Association for Computational Linguistics.
- Cite (Informal):
- Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models (Kayal et al., BioNLP 2017)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/W17-2327.pdf
- Data
- BioASQ