Marius Doornenbal
2017
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models
Subhradeep Kayal
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Zubair Afzal
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George Tsatsaronis
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Sophia Katrenko
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Pascal Coupet
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Marius Doornenbal
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Michelle Gregory
BioNLP 2017
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.
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Co-authors
- Subhradeep Kayal 1
- Zubair Afzal 1
- George Tsatsaronis 1
- Sophia Katrenko 1
- Pascal Coupet 1
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