{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:18:28Z","timestamp":1753355908075,"version":"3.41.0"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Major Special Project of Anhui Science and Technology Department","award":["18030801133"],"award-info":[{"award-number":["18030801133"]}]},{"name":"Science and Technology Service Network Initiative","award":["KFJ-STS-ZDTP-079"],"award-info":[{"award-number":["KFJ-STS-ZDTP-079"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,3,31]]},"abstract":"<jats:p>Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference between English and Chinese. Some researchers have noticed the importance of Chinese NER and used the recurrent neural network or convolutional neural network (CNN) to deal with this task. However, it is interesting to know whether the performance could be improved if the advantages of the RNN and CNN can be both utilized. Moreover, RoBERTa-WWM, as a pre-training model, can generate the embeddings with word-level features, which is more suitable for Chinese NER compared with Word2Vec. In this article, we propose a hybrid model. This model first obtains the entities identified by bidirectional long short-term memory and CNN, respectively, and then uses two hybrid strategies to output the final results relying on these entities. We also conduct experiments on raw medical records from real hospitals. This dataset is provided by the China Conference on Knowledge Graph and Semantic Computing in 2019 (CCKS 2019). Results demonstrate that the hybrid model can improve performance significantly.<\/jats:p>","DOI":"10.1145\/3436819","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T16:55:14Z","timestamp":1619196914000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A Hybrid Model for Named Entity Recognition on Chinese Electronic Medical Records"],"prefix":"10.1145","volume":"20","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Intelligent Machines and University of Science and Technology of China, Hefei City, Anhui Province, China"}]},{"given":"Yining","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines and University of Science and Technology of China, Hefei City, Anhui Province, China"}]},{"given":"Zuchang","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines"}]},{"given":"Lisheng","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines"}]},{"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Machines, Anhui Province, China"}]}],"member":"320","published-online":{"date-parts":[[2021,4,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919121"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103290"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1091"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2852004"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886311"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13326-018-0179-8"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.650"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367722.3367786"},{"volume-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 473","author":"Abhyuday","key":"e_1_2_1_9_1","unstructured":"Abhyuday N. Jagannatha and Hong Yu. 2016. Bidirectional RNN for medical event detection in electronic health records . In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 473 . Abhyuday N. Jagannatha and Hong Yu. 2016. Bidirectional RNN for medical event detection in electronic health records. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 473."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1136\/jamia.1994.95236146"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the Computional Terminology for Medical and Biological Applications Workshop of the 2nd International Conference on NLP.","author":"Gaizauskas Robert","year":"2000","unstructured":"Robert Gaizauskas , George Demetriou , and Kevin Humphreys . 2000 . Term recognition and classification in biological science journal articles . In Proceedings of the Computional Terminology for Medical and Biological Applications Workshop of the 2nd International Conference on NLP. Robert Gaizauskas, George Demetriou, and Kevin Humphreys. 2000. Term recognition and classification in biological science journal articles. In Proceedings of the Computional Terminology for Medical and Biological Applications Workshop of the 2nd International Conference on NLP."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073163"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045367"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2016.02.011"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298023.3298048"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx228"},{"key":"e_1_2_1_17_1","first-page":"624","article-title":"Named entity recognition in Chinese clinical text using deep neural network","volume":"216","author":"Wu Yonghui","year":"2015","unstructured":"Yonghui Wu , Min Jiang , Jianbo Lei , and Hua Xu . 2015 . Named entity recognition in Chinese clinical text using deep neural network . Studies in Health Technology and Informatics 216 (2015), 624 . Yonghui Wu, Min Jiang, Jianbo Lei, and Hua Xu. 2015. Named entity recognition in Chinese clinical text using deep neural network. Studies in Health Technology and Informatics 216 (2015), 624.","journal-title":"Studies in Health Technology and Informatics"},{"volume-title":"Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data","author":"Zhou Peng","key":"e_1_2_1_18_1","unstructured":"Peng Zhou , Suncong Zheng , Jiaming Xu , Zhenyu Qi , Hongyun Bao , and Bo Xu. 2017. Joint extraction of multiple relations and entities by using a hybrid neural network . In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data . Springer , 135\u2013146. Peng Zhou, Suncong Zheng, Jiaming Xu, Zhenyu Qi, Hongyun Bao, and Bo Xu. 2017. Joint extraction of multiple relations and entities by using a hybrid neural network. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, 135\u2013146."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-15-S1-S9"},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Jun Liang Xuemei Xian Xiaojun He Meifang Xu Sheng Dai Jun\u2019yi Xin Jie Xu Jian Yu and Jianbo Lei. 2017. A novel approach towards medical entity recognition in Chinese clinical text. Journal of Healthcare Engineering. Epub 2017 July 5.  Jun Liang Xuemei Xian Xiaojun He Meifang Xu Sheng Dai Jun\u2019yi Xin Jie Xu Jian Yu and Jianbo Lei. 2017. A novel approach towards medical entity recognition in Chinese clinical text. Journal of Healthcare Engineering. Epub 2017 July 5.","DOI":"10.1155\/2017\/4898963"},{"key":"e_1_2_1_21_1","volume-title":"CEUR Workshop Proceedings","volume":"1976","author":"Li Zhenzhen","year":"2017","unstructured":"Zhenzhen Li , Qun Zhang , Yang Liu , Dawei Feng , and Zhen Huang . 2017 . Recurrent neural networks with specialized word embedding for Chinese clinical named entity recognition . In CEUR Workshop Proceedings , Vol. 1976 . 55\u201360. Zhenzhen Li, Qun Zhang, Yang Liu, Dawei Feng, and Zhen Huang. 2017. Recurrent neural networks with specialized word embedding for Chinese clinical named entity recognition. In CEUR Workshop Proceedings, Vol. 1976. 55\u201360."},{"key":"e_1_2_1_22_1","volume-title":"CEUR Workshop Proceedings","volume":"1976","author":"Xia Yuhang","year":"2017","unstructured":"Yuhang Xia and Qi Wang . 2017 . Clinical named entity recognition: ECUST in the CCKS-2017 shared task 2 . In CEUR Workshop Proceedings , Vol. 1976 . 43\u201348. Yuhang Xia and Qi Wang. 2017. Clinical named entity recognition: ECUST in the CCKS-2017 shared task 2. In CEUR Workshop Proceedings, Vol. 1976. 43\u201348."},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 3384\u20133393","author":"Zhu Yuying","year":"2019","unstructured":"Yuying Zhu and Guoxin Wang . 2019 . CAN-NER: Convolutional attention network for Chinese named entity recognition . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 3384\u20133393 . Yuying Zhu and Guoxin Wang. 2019. CAN-NER: Convolutional attention network for Chinese named entity recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers). 3384\u20133393."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.01.004"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103133"},{"key":"e_1_2_1_26_1","first-page":"1","volume-title":"Retrieved","author":"Rui Qiao Wenkang Huang","year":"2021","unstructured":"Wenkang Huang Rui Qiao , Xiaoran Yang . Medical Named Entity Recognition Based on BERT and Model Fusion. n.d . Retrieved January 30, 2021 from https:\/\/conference.bj.bcebos.com\/ccks2019\/eval\/webpage\/pdfs\/eval_paper_1_1_ 1 .pdf. Wenkang Huang Rui Qiao, Xiaoran Yang. Medical Named Entity Recognition Based on BERT and Model Fusion. n.d. Retrieved January 30, 2021 from https:\/\/conference.bj.bcebos.com\/ccks2019\/eval\/webpage\/pdfs\/eval_paper_1_1_1.pdf."},{"key":"e_1_2_1_27_1","unstructured":"Xiaoya Li Yuxian Meng Xiaofei Sun Qinghong Han Arianna Yuan and Jiwei Li. 2019. Is word segmentation necessary for deep learning of Chinese representations? arXiv:1905.05526  Xiaoya Li Yuxian Meng Xiaofei Sun Qinghong Han Arianna Yuan and Jiwei Li. 2019. Is word segmentation necessary for deep learning of Chinese representations? arXiv:1905.05526"},{"key":"e_1_2_1_28_1","volume-title":"Toutanova","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina N . Toutanova . 2018 . BERT : Pre-training of de ep bidirectional transformers for language understanding. arXiv:1810.04805 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova. 2018. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805"},{"key":"e_1_2_1_29_1","unstructured":"Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781  Tomas Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999959"},{"volume-title":"n.d. ymcui\/Chinese-BERT-wwm. Retrieved","year":"2021","key":"e_1_2_1_31_1","unstructured":"GitHub. n.d. ymcui\/Chinese-BERT-wwm. Retrieved January 30, 2021 from https:\/\/github.com\/ymcui\/Chinese-BERT-wwm. GitHub. n.d. ymcui\/Chinese-BERT-wwm. Retrieved January 30, 2021 from https:\/\/github.com\/ymcui\/Chinese-BERT-wwm."},{"key":"e_1_2_1_32_1","unstructured":"Zachary C. Lipton John Berkowitz and Charles Elkan. 2015. A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019  Zachary C. Lipton John Berkowitz and Charles Elkan. 2015. A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_1_34_1","unstructured":"Z. Huang W. Xu and K. Yu. 2019. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991  Z. Huang W. Xu and K. Yu. 2019. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935223"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.5555\/645530.655813"},{"key":"e_1_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv:1408.5882  Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv:1408.5882","DOI":"10.3115\/v1\/D14-1181"},{"volume-title":"Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data","author":"Zhou Peng","key":"e_1_2_1_38_1","unstructured":"Peng Zhou , Suncong Zheng , Jiaming Xu , Zhenyu Qi , Hongyun Bao , and Bo Xu. 2017. Joint extraction of multiple relations and entities by using a hybrid neural network . In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data . Springer , 135\u2013146. Peng Zhou, Suncong Zheng, Jiaming Xu, Zhenyu Qi, Hongyun Bao, and Bo Xu. 2017. Joint extraction of multiple relations and entities by using a hybrid neural network. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, 135\u2013146."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295349"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3436819","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3436819","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T17:45:05Z","timestamp":1750268705000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3436819"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,31]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,3,31]]}},"alternative-id":["10.1145\/3436819"],"URL":"https:\/\/doi.org\/10.1145\/3436819","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"type":"print","value":"2375-4699"},{"type":"electronic","value":"2375-4702"}],"subject":[],"published":{"date-parts":[[2021,3,31]]},"assertion":[{"value":"2020-05-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}