Abstract
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.
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.
| Original language | English |
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| Title of host publication | Findings of the Association for Computational Linguistics |
| Subtitle of host publication | ACL-IJCNLP 2021 |
| Editors | Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli |
| Place of Publication | United States |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 2674–2685 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781954085541 |
| DOIs | |
| Publication status | Published - Aug 2021 |
| Event | The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) - Virtual, Bangkok, Malaysia Duration: 01 Aug 2021 → 06 Aug 2021 https://2021.aclweb.org/ |
Conference
| Conference | The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) |
|---|---|
| Country/Territory | Malaysia |
| City | Bangkok |
| Period | 01/08/21 → 06/08/21 |
| Other | The great event will be jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP) and held in Berkeley Hotel in Bangkok, Thailand, during August 1-6, 2021. For the first time, the Association for Computational Linguistics and the Asian Federation of Natural Language Processing are pleased to announce that their joint meeting takes place in Thailand. The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) will be held in Bangkok, Thailand, during August 1-6, 2021. As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference. |
| Internet address |