{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T17:40:44Z","timestamp":1772473244039,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"17","license":[{"start":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:00:00Z","timestamp":1615420800000},"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0910404"],"award-info":[{"award-number":["2018YFC0910404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772409"],"award-info":[{"award-number":["61772409"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Research Group of the National Natural Science Foundation of China","award":["61721002"],"award-info":[{"award-number":["61721002"]}]},{"name":"Innovation Research Team of Ministry of Education","award":["IRT_17R86"],"award-info":[{"award-number":["IRT_17R86"]}]},{"name":"Project of China Knowledge Centre for Engineering Science and Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events\u2019 attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code will be made available at https:\/\/github.com\/zxy951005\/KB-CR upon publication. Data is avaliable at http:\/\/2011.bionlp-st.org\/ and https:\/\/github.com\/UCDenver-ccp\/CRAFT\/releases\/tag\/v3.1.3.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab153","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T20:12:44Z","timestamp":1615320764000},"page":"2699-2705","source":"Crossref","is-referenced-by-count":13,"title":["Knowledge enhanced LSTM for coreference resolution on biomedical texts"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5207-5336","authenticated-orcid":false,"given":"Yufei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, School of Computer Science and Technology, Xi\u2019an 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