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TCM patents contain rich medical, legal, and economic information. Effective text mining of TCM patents is of great theoretical and practical significance (e.g., the R&amp;D of new medicines, patent infringement litigation, and patent acquisition). Named entity recognition (NER) is a fundamental task in natural language processing and a crucial step before indepth analysis of TCM patent. In this paper, a method combining Bidirectional Long Short\u2010Term Memory neural network with Conditional Random Field (BiLSTM\u2010CRF) is proposed to automatically recognize entities of interest (i.e., herb names, disease names, symptoms, and therapeutic effects) from the abstract texts of TCM patents. By virtue of the capabilities of deep learning methods, the semantic information in the context can be learned without feature engineering. Experiments show that the BiLSTM\u2010CRF\u2010based method provides superior performance in comparison with various baseline methods.<\/jats:p>","DOI":"10.1155\/2021\/6696205","type":"journal-article","created":{"date-parts":[[2021,6,2]],"date-time":"2021-06-02T22:29:17Z","timestamp":1622672957000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Named Entity Recognition of Traditional Chinese Medicine Patents Based on BiLSTM\u2010CRF"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4413-2625","authenticated-orcid":false,"given":"Na","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5199-862X","authenticated-orcid":false,"given":"Hao","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6393-8472","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,6,2]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym11020165"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20020539"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2952644"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-019-04266-y"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-019-02816-7"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002381"},{"key":"e_1_2_12_7_2","doi-asserted-by":"crossref","unstructured":"RahmanH. 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