{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:15:37Z","timestamp":1775002537277,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"15","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"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\/100016804","name":"Natural Science Foundation of Shenzhen City","doi-asserted-by":"crossref","award":["JCYJ20180306172131515"],"award-info":[{"award-number":["JCYJ20180306172131515"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neural networks on a single dataset mostly depends on data quality and quantity while high-quality data tends to be limited in size. To alleviate task-specific data limitation, some studies explored the multi-task learning (MTL) for Bio-NER and achieved state-of-the-art performance. However, these MTL methods did not make full use of information from various datasets of Bio-NER. The performance of state-of-the-art MTL method was significantly limited by the number of training datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose two dataset-aware MTL approaches for Bio-NER which jointly train all models for numerous Bio-NER datasets, thus each of these models could discriminatively exploit information from all of related training datasets. Both of our two approaches achieve substantially better performance compared with the state-of-the-art MTL method on 14 out of 15 Bio-NER datasets. Furthermore, we implemented our approaches by incorporating Bio-NER and biomedical part-of-speech (POS) tagging datasets. The results verify Bio-NER and POS can significantly enhance one another.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Our source code is available at https:\/\/github.com\/zmmzGitHub\/MTL-BC-LBC-BioNER and all datasets are publicly available at https:\/\/github.com\/cambridgeltl\/MTL-Bioinformatics-2016.<\/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\/btaa515","type":"journal-article","created":{"date-parts":[[2020,5,12]],"date-time":"2020-05-12T03:19:29Z","timestamp":1589253569000},"page":"4331-4338","source":"Crossref","is-referenced-by-count":13,"title":["Dataset-aware multi-task learning approaches for biomedical named entity recognition"],"prefix":"10.1093","volume":"36","author":[{"given":"Mei","family":"Zuo","sequence":"first","affiliation":[{"name":"College of Science, Harbin Institute of Technology , Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3503-5161","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, Harbin Institute of Technology , Shenzhen 518055, China"}]}],"member":"286","published-online":{"date-parts":[[2020,5,16]]},"reference":[{"key":"2023062312041738700_btaa515-B1","first-page":"101","author":"Ando","year":"2007"},{"key":"2023062312041738700_btaa515-B2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","article-title":"Multitask learning","volume":"28","author":"Caruana","year":"1997","journal-title":"Mach. 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