{"url":"/task/medical-relation-extraction","name":"Medical Relation Extraction","slug":"medical-relation-extraction","description_markdown":"Biomedical relation extraction is the task of detecting and classifying semantic relationships from biomedical text.","categories":[{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":15,"papers_with_code":9,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":5,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/medical-relation-extraction-on-ddi-extraction","slug":"medical-relation-extraction-on-ddi-extraction","dataset":"DDI extraction 2013 corpus","dataset_url":"/dataset/ddi","rows_in_archive":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"BioLinkBERT (large)","paper_title":"LinkBERT: Pretraining Language Models with Document Links","paper_url":"/paper/linkbert-pretraining-language-models-with","paper_date":"2022-03-29","arxiv_id":"2203.15827","code_links":[{"title":"michiyasunaga/LinkBERT","url":"https://github.com/michiyasunaga/LinkBERT"}],"syntology":{"n":14,"n_ran":0,"n_unverified":14,"n_pointer_only":0}}},{"leaderboard":"/sota/medical-relation-extraction-on-cmeie","slug":"medical-relation-extraction-on-cmeie","dataset":"CMeIE","dataset_url":"/dataset/cmeie","rows_in_archive":1,"metrics":["Micro F1"],"first_row_in_archive_order":{"model":"RoBERTa-wwm-ext-large","paper_title":"CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark","paper_url":"/paper/cblue-a-chinese-biomedical-language","paper_date":"2021-06-15","arxiv_id":"2106.08087","code_links":[{"title":"cbluebenchmark/cblue","url":"https://github.com/cbluebenchmark/cblue"},{"title":"freedomintelligence/sdak","url":"https://github.com/freedomintelligence/sdak"}],"syntology":{"n":16,"n_ran":4,"n_unverified":12,"n_pointer_only":0}}}],"datasets":[{"url":"/dataset/radgraph","name":"RadGraph","full_name":"RadGraph: Extracting Clinical Entities and Relations from Radiology Reports","num_papers_in_archive":78},{"url":"/dataset/ddi","name":"DDI","full_name":"","num_papers_in_archive":54},{"url":"/dataset/gad","name":"GAD","full_name":"Gene Associations Database","num_papers_in_archive":5},{"url":"/dataset/cmeie","name":"CMeIE","full_name":"Chinese Medical Information Extraction Dataset","num_papers_in_archive":1},{"url":"/dataset/eu-adr","name":"EU-ADR","full_name":"EU-ADR","num_papers_in_archive":0}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":9,"of":9,"tagged_in_all":15,"items":[{"url":"/paper/biobert-a-pre-trained-biomedical-language","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","date":"2019-01-25","arxiv_id":"1901.08746","repositories_listed":19,"syntology":{"n":25,"n_ran":4,"n_unverified":21,"n_pointer_only":1}},{"url":"/paper/transfer-learning-in-biomedical-natural","title":"Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets","date":"2019-06-13","arxiv_id":"1906.05474","repositories_listed":4,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/cblue-a-chinese-biomedical-language","title":"CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark","date":"2021-06-15","arxiv_id":"2106.08087","repositories_listed":2,"syntology":{"n":16,"n_ran":4,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/supporting-medical-relation-extraction-via","title":"Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest","date":"2022-08-29","arxiv_id":"2208.13472","repositories_listed":1,"syntology":null},{"url":"/paper/linkbert-pretraining-language-models-with","title":"LinkBERT: Pretraining Language Models with Document Links","date":"2022-03-29","arxiv_id":"2203.15827","repositories_listed":1,"syntology":{"n":14,"n_ran":0,"n_unverified":14,"n_pointer_only":0}},{"url":"/paper/leveraging-dependency-forest-for-neural-1","title":"Leveraging Dependency Forest for Neural Medical Relation Extraction","date":"2019-11-11","arxiv_id":"1911.04123","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-deep-learning-approach-for-medical","title":"A hybrid deep learning approach for medical relation extraction","date":"2018-06-26","arxiv_id":"1806.11189","repositories_listed":1,"syntology":null},{"url":"/paper/drug-drug-interaction-extraction-from","title":"Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network","date":"2017-01-28","arxiv_id":"1701.08303","repositories_listed":1,"syntology":null},{"url":"/paper/crowdsourcing-ground-truth-for-medical","title":"Crowdsourcing Ground Truth for Medical Relation Extraction","date":"2017-01-09","arxiv_id":"1701.02185","repositories_listed":1,"syntology":null}],"syntology_records":4,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}