GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction

As part of scientific articles, grant information refers to funder names and their corresponding grant numbers. Extracting such funding information from articles is of significant importance to both academic and funding bodies. The studies on this topic face two major challenges: 1) no high-quality benchmark datasets; and 2) difficulties in extracting complex relationships between funders and grantIDs. In this paper, we present a novel pipeline framework called GrantRel, which consists of a funding sentence classifier, as well as a joint entity and relation extractor. For this purpose, we manually label two high-quality datasets called Grant-SP and Grant-RE, respectively. In addition, our relation extraction (RE) model uses both position embedding and context embedding in an adaptive-learning way. The experiment results have demonstrated that our model outperforms several state-of-the-art BERT-based RE baselines as higher as 6.5% of F1 scores against the PubMed Central (PMC) test set and 3.5% of that against the arXiv test set.