[1]HAMMERTON J. Named entity recognition with long short-term memory[C]//Proceedings of the Seventh Conference on Natural Language Learning at Annual Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies 2003. 2003:172-175.
[2]LAMPLE G,BALLESTEROS M,SUBRAMANIAN S,et al. Neural architectures for named entity recognition[J]. arXiv preprint arXiv:1603.01360,2016.
[3]ZHANG Y,YANG J. Chinese NER using lattice LSTM[J]. arXiv preprint arXiv:1805.02023,2018.
[4]GUI T,MA R,ZHANG Q,et al. CNN-Based Chinese NER with Lexicon Rethinking[C]//Twenty-Eighth International Joint Conference on Artificial Intelligence. Macao:Springer,2019:4982-4988.
[5]WANG C Q,CHEN W,XU B. Named entity recognition with gated convolutional neural networks[M]//Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer,Cham,2017:110-121.
[6]YU F,KOLTUN V. Multi-scale context aggregation by dilated convolutions[J]. arXiv preprint arXiv:1511.07122,2015.
[7]YU B H,WEI J X. IDCNN-CRF-based domain named entity recognition method[C]//2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology(IEEE 2nd International Conference on Civil Aviation Safety and Information Technology:542-546.
[8]杨晓辉,毕雪华,张琳琳,等. 基于多任务的中文电子病历中命名实体识别研究[J]. 东北师大学报(自然科学版),2020,52(1):81-87. DOI:10.16163/j.cnki.22-1123/n.2020.01.016.
[9]孙弋,梁兵涛. 基于BERT和多头注意力的中文命名实体识别方法[J/OL]. 重庆邮电大学学报(自然科学版):1-10[2022-02-13]. http://kns.cnki.net/kcms/detail/50.1181.N.20211209.2010.004.html.
[10]张柯文,李翔,严云洋,等. 基于多特征双向门控神经网络的领域专家实体抽取方法[J]. 南京师大学报(自然科学版),2021,44(1):128-135.
[11]孔祥鹏,吾守尔·斯拉木,杨启萌,等. 基于迁移学习的维吾尔语命名实体识别[J]. 东北师大学报(自然科学版),2020,52(2):58-65. DOI:10.16163/j.cnki.22-1123/n.2020.02.010.
[12]李妮,关焕梅,杨飘,等. 基于BERT-IDCNN-CRF的中文命名实体识别方法[J]. 山东大学学报(理学版),2020,55(1):102-109.
[13]周志华. 机器学习[M]. 北京:清华大学出版社,2016.
[14]石春丹,秦岭. 基于BGRU-CRF的中文命名实体识别方法[J]. 计算机科学,2019,46(9):237-242.
[15]杨飘,董文永. 基于BERT嵌入的中文命名实体识别方法[J]. 计算机工程,2020,46(4):40-45,52. DOI:10.19678/j.issn.1000-3428.0054272.