{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T16:48:13Z","timestamp":1787503693636,"version":"build-2736575974"},"reference-count":17,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Methods"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.ymeth.2024.04.013","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T11:55:53Z","timestamp":1713354953000},"page":"71-77","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Language model based on deep learning network for biomedical named entity recognition"],"prefix":"10.1016","volume":"226","author":[{"given":"Guan","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhao","family":"Jian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongwen","family":"Quan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ymeth.2024.04.013_b0005","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab282","article-title":"Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison","author":"Song","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.ymeth.2024.04.013_b0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114856","article-title":"Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory","volume":"176","author":"Santoso","year":"2021","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"10.1016\/j.ymeth.2024.04.013_b0015","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1093\/bib\/bbaa054","article-title":"Biomedical named entity recognition and linking datasets: survey and our recent development","volume":"21","author":"Huang","year":"2020","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.ymeth.2024.04.013_b0020","series-title":"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics","article-title":"Recurrent neural network models for disease name recognition using domain invariant features","author":"Sahu","year":"2016"},{"key":"10.1016\/j.ymeth.2024.04.013_b0025","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1093\/bioinformatics\/btx228","article-title":"Deep learning with word embeddings improves biomedical named entity recognition","volume":"33","author":"Habibi","year":"2017","journal-title":"Bioinformatics"},{"key":"10.1016\/j.ymeth.2024.04.013_b0030","article-title":"Neural architectures for named entity recognition","author":"Lample","year":"2016","journal-title":"HLT-NAACL"},{"key":"10.1016\/j.ymeth.2024.04.013_b0035","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1162\/tacl_a_00104","article-title":"Named entity recognition with bidirectional LSTM-CNNs","volume":"4","author":"Chiu Jason","year":"2016","journal-title":"Transactions of the Association for Computational Linguistics"},{"issue":"1","key":"10.1016\/j.ymeth.2024.04.013_b0040","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. Learn."},{"issue":"1","key":"10.1016\/j.ymeth.2024.04.013_b0045","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1186\/s12859-017-1776-8","article-title":"A neural network multi-task learning approach to biomedical named entity recognition","volume":"18","author":"Crichton","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.ymeth.2024.04.013_b0050","article-title":"Multi-task cross-lingual sequence tagging from scratch","author":"Yang","year":"2016","journal-title":"arXiv:1603.06270"},{"issue":"10","key":"10.1016\/j.ymeth.2024.04.013_b0055","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1093\/bioinformatics\/bty869","article-title":"Cross-type biomedical named entity recognition with deep multi-task learning","volume":"35","author":"Wang","year":"2019","journal-title":"Bioinformatics"},{"issue":"10","key":"10.1016\/j.ymeth.2024.04.013_b0060","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1186\/s12859-019-2813-6","article-title":"CollaboNet: collaboration of deep neural networks for biomedical named entity recognition","volume":"20","author":"Yoon","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"8","key":"10.1016\/j.ymeth.2024.04.013_b0065","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"issue":"1","key":"10.1016\/j.ymeth.2024.04.013_b0070","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1186\/s12859-017-1776-8","article-title":"A neural network multi-task learning approach to biomedical named entity recognition","volume":"18","author":"Crichton","year":"2017","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.ymeth.2024.04.013_b0075","series-title":"Proceedings of the 5th International Symposium on Languages in Biology and Medicine","article-title":"Distributional semantics resources for biomedical text processing","author":"Pyysalo","year":"2013"},{"key":"10.1016\/j.ymeth.2024.04.013_b0080","series-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(ACL)","article-title":"Semi-supervised sequence tagging with bidirectional language models","author":"Peters","year":"2017"},{"key":"10.1016\/j.ymeth.2024.04.013_b0085","article-title":"Deep contextualized word representations","author":"Peters","year":"2018","journal-title":"Proceedings of NAACL-HLT"}],"container-title":["Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1046202324001038?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1046202324001038?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T11:18:30Z","timestamp":1716031110000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1046202324001038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":17,"alternative-id":["S1046202324001038"],"URL":"https:\/\/doi.org\/10.1016\/j.ymeth.2024.04.013","relation":{},"ISSN":["1046-2023"],"issn-type":[{"value":"1046-2023","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Language model based on deep learning network for biomedical named entity recognition","name":"articletitle","label":"Article Title"},{"value":"Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ymeth.2024.04.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}