Yoon, Wonjin, Sean Yi, Richard Jackson, Hyunjae Kim, Sunkyu Kim, and Jaewoo Kang.
Biomedical relation extraction with knowledge base refined weak-supervision (Submitted to DATABASE). 2022
* Presented under the name Using Knowledge Base to Refine Data Augmentation for Biomedical Relation Extraction at the BioCreative VII workshop
Predictions:
http://nlp.dmis.korea.edu/projects/drugprot-yoon-et-al-2022/runID2-predictions.tar.gz
(105M) SHA1SUM fbce7aab9e9a370f59ceb62f316e9ebd22f8e7f7
Test dataset (includes plain texts):
http://nlp.dmis.korea.edu/projects/drugprot-yoon-et-al-2022/test_large.tar.gz
(2.1G) SHA1SUM f5c52b621ed25555a9eccbbf2aa4e5cdaad5f892
Our model:
Yoon, Wonjin, Sean Yi, Richard Jackson, Hyunjae Kim, Sunkyu Kim, and Jaewoo Kang. Biomedical relation extraction with knowledge base refined weak-supervision. (Submitted to DATABASE). 2022
Dataset:
Antonio Miranda, Farrokh Mehryary, Jouni Luoma, SampoPyysalo, Alfonso Valencia, and Martin Krallinger. Overview of drugprot biocreative vii track: quality evaluation andlarge scale text mining of drug-gene/protein relations. InProceedings of the seventh BioCreative challenge evaluation workshop, 2021.
This work partially used Comparative Toxicogenomics Database (CTD). We appreciate their contribution towards BioNLP/Bioinformatics society :
Patrick Verga, Emma Strubell, and Andrew McCallum.Simultaneously self-attending to all mentions for full-abstract biological relation extraction.InProceedingsof the 2018 Conference of the North American Chapterof the Association for Computational Linguistics: HumanLanguage Technologies, Volume 1 (Long Papers), pages872–884, New Orleans, Louisiana, June 2018. Association for Computational Linguistics.
Users of our resources must understand that resources we share, including automatic annotations andmodels, are not designed for real-world use nor reviewed from domain experts. Using the resource should be at your own risks.
TBA: Will be one of open source license