{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-dataset-for-n-ary-relation-extraction-of-1","title":"A Dataset for N-ary Relation Extraction of Drug Combinations","arxiv_id":"2205.02289","date":"2022-05-04","proceeding":"NAACL 2022 7","authors":["Aryeh Tiktinsky","Vijay Viswanathan","Danna Niezni","Dana Meron Azagury","Yosi Shamay","Hillel Taub-Tabib","Tom Hope","Yoav Goldberg"],"abstract":"Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a promising baseline model and identify clear areas for further improvement. We release our dataset, code, and baseline models publicly to encourage the NLP community to participate in this task.","url_abs":"https://arxiv.org/abs/2205.02289v1","url_pdf":"https://arxiv.org/pdf/2205.02289v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-dataset-for-n-ary-relation-extraction-of-1","repo_url":"https://github.com/allenai/drug-combo-extraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-dataset-for-n-ary-relation-extraction-of-1","repo_url":"https://github.com/bionlproc/end-to-end-combdrugext","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"drug-drug-interaction-extraction","task_name":"Drug–drug Interaction Extraction"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"relation-extraction","task_name":"Relation Extraction"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[{"slug":"drug-combination-extraction-dataset","name":"Drug Combination Extraction Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-drug-interaction-extraction-on-drug","task":"Drug–drug Interaction Extraction","dataset":"Drug Combination Extraction Dataset","model":"PubmedBERT + PURE (domain-adapted)","rank_in_archive_order":2,"of":2,"metrics":{"Exact Match F1 (\"Any Combination\")":"69.4","Exact Match F1 (\"Positive Combination\")":"61.8"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2205.02289","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}