{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:30:55Z","timestamp":1780774255483,"version":"3.54.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702121"],"award-info":[{"award-number":["61702121"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772378"],"award-info":[{"award-number":["61772378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Foundation of Ministry of Education of China","award":["18JZD015"],"award-info":[{"award-number":["18JZD015"]}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2020A151501705"],"award-info":[{"award-number":["2020A151501705"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint models are built upon sequential scheme, and fail to detect overlapping entity and relation, which are ubiquitous in biomedical texts. The main reason is that sequential models have relatively weaker power in capturing long-range dependencies, which results in lower performance in encoding longer sentences. In this article, we propose a novel span-graph neural model for jointly extracting overlapping entity relation in biomedical texts. Our model treats the task as relation triplets prediction, and builds the entity-graph by enumerating possible candidate entity spans. The proposed model captures the relationship between the correlated entities via a span scorer and a relation scorer, respectively, and finally outputs all valid relational triplets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Experimental results on two biomedical entity relation extraction tasks, including drug\u2013drug interaction detection and protein\u2013protein interaction detection, show that the proposed method outperforms previous models by a substantial margin, demonstrating the effectiveness of span-graph-based method for overlapping relation extraction in biomedical texts. Further in-depth analysis proves that our model is more effective in capturing the long-range dependencies for relation extraction compared with the sequential models.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Related codes are made publicly available at http:\/\/github.com\/Baxelyne\/SpanBioER.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa993","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T20:12:39Z","timestamp":1605643959000},"page":"1581-1589","source":"Crossref","is-referenced-by-count":32,"title":["A span-graph neural model for overlapping entity relation extraction in biomedical texts"],"prefix":"10.1093","volume":"37","author":[{"given":"Hao","family":"Fei","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University , Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University , Hangzhou 310024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafeng","family":"Ren","sequence":"additional","affiliation":[{"name":"Laboratory of Language and Artificial Intelligence, Guangdong University of Foreign Studies , Guangzhou 510420, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghong","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University , Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"2023051709450307400_btaa993-B1","first-page":"224","volume-title":"Proceedings of 2019 IEEE International Conference on Semantic Computing","author":"Ahmed","year":"2019"},{"key":"2023051709450307400_btaa993-B2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.artmed.2004.07.016","article-title":"Comparative experiments on learning information extractors for proteins and their interactions","volume":"33","author":"Bunescu","year":"2005","journal-title":"Artif. 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