{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:12:42Z","timestamp":1750219962470,"version":"3.41.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>The recognition of entities in an electronic medical record (EMR) is especially important to downstream tasks, such as clinical entity normalization and medical dialogue understanding. However, in the medical professional field, training a high-quality named entity recognition system always requires large-scale annotated datasets, which are highly expensive to obtain. In this article, to lower the cost of data annotation and maximizing the use of unlabeled data, we propose a hybrid approach to recognizing the entities in Chinese electronic medical record, which is in combination of loss-based active learning and semi-supervised learning. Specifically, we adopted a dynamic balance strategy to dynamically balance the minimum loss predicted by a named entity recognition decoder and a loss prediction module at different stages in the process. Experimental results demonstrated our proposed framework\u2019s effectiveness and efficiency, achieving higher performances than existing approaches on Chinese EMR entity recognition datasets under limited labeling resources.<\/jats:p>","DOI":"10.1145\/3588314","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T12:02:42Z","timestamp":1679313762000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Combination of Loss-based Active Learning and Semi-supervised Learning for Recognizing Entities in Chinese Electronic Medical Records"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8085-7586","authenticated-orcid":false,"given":"Jinghui","family":"Yan","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Technology, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9864-3818","authenticated-orcid":false,"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Technology, Beijing Jiaotong University, National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0170-626X","authenticated-orcid":false,"given":"Jinan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Technology, Beijing Jiaotong University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1306","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Carlson Andrew","year":"2010","unstructured":"Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam R. 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