[1] 张晓艳,王挺,陈火旺. 命名实体识别研究[J]. 计算机科学,2005,32(4): 44-48.
Zhang X Y, Wang T, Chen H W. Research on Named Entity Recognition[J]. Computer Science,2005,32(4): 44-48.
[2] 何玉洁,杜方,史英杰,等. 基于深度学习的命名实体识别研究综述[J]. 计算机工程与应用,2021,57(11):21-36.
He Y J, Du F, Shi Y J, et al. Survey of Named Entity Recognition Based on Deep Learning[J]. Computer Engineering and Application, 2021, 57(11): 21-36.
[3] 王月,王孟轩,张胜,等. 基于BERT的警情文本命名实体识别[J]. 计算机应用,2020,40(2):535-540.
Wang Y, Wang M X, Zhang S, et al. Alarm text named entity recognition based on BERT[J]. Journal of Computer Applications, 2020, 40(2): 535-540.
[4] Zhang Y, Yang J. Chinese NER Using Lattice LSTM[C]//In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018.
[5] Isozaki H, Kazawa H. Efficient support vector classifiers for named entity recognition[C]//COLING 2002. The 19th International Conference on Computational Linguistics. 2002.
[6] Liu K, Hu Q, Liu J, et al. Named entity recognition in Chinese electronic medical records based on CRF[C]//2017 14th Web Information Systems and Applications Conference (WISA). IEEE, 2017: 105-110.
[7] Han A.L.F, Wong D.F, Chao L.S. Chinese named entity recognition with conditional random fields in the light of Chinese characteristics[C]//In Proceedings of the Intelligent Information Systems Symposium, June 2013, Springer: Berlin/Heidelberg, Germany. pp. 57-68.
[8] Morwal S, Jahan N, Chopra D. Named entity recognition using hidden Markov model (HMM)[J]. International Journal on Natural Language Computing (IJNLC), 2012.
[9] Fu G H, Luke K K. Chinese named entity recognition using lexicalized HMMs[J]. ACM SIGKDD Explorations Newsletter, 2005, 7(1): 19-25.
[10] Bender O, Och F J, Ney H. Maximum entropy models for named entity recognition[C]//Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. 2003: 148-151.
[11] Chieu H L, Ng H T. Named entity recognition: a maximum entropy approach using global information[C]//COLING 2002: The 19th International Conference on Computational Linguistics. 2002.
[12] 吴超,王汉军. 基于GRU的电力调度领域命名实体识别方法[J]. 计算机系统应用,2020, 29(8):185-191.
Wu C, Wang H J. Named Entity Recognition in Electric Power Dispatching Field Based on GRU[J]. Computer Systems & Applications, 2020, 29(8): 185-191.
[13] Dong C H, Wu H J, Zhang J J, et al. Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media[C]//In Proceedings of the China National Conference on Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, 2017; pp. 197-208.
[14] Wu F Z, Liu J X, Wu C H, et al. Neural Chinese named entity recognition via CNN-LSTM-CRF and joint training with word segmentation[C]//The World Wide Web Conference. 2019: 3342-3348.
[15] Dong C H, Zhang J J, Zong C Q, et al. Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition[C]//In Proceedings of Natural Language Understanding and Intelligent Applications, Springer, Cham, 2016; pp. 239–250.
[16] Tang B Z, Wang X L, Yan J, et al. Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF[J]. BMC medical informatics and decision making, 2019, 19(3): 89-97.
[17] Huang Z H, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv preprint arXiv:1508.01991, 2015.
[18] Chen Y, Zhou C J, Li T X, et al. Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training[J]. Journal of biomedical informatics, 2019, 96: 103252.
[19] 李一斌,张欢欢. 基于双向GRU-CRF的中文包装产品实体识别[J]. 华东理工大学学报(自然科学版),2019, 45, 486-490.
Li Y B, Zhang H H. Chinese Packaging Product Entity Recognition Based on Bidirectional GRU-CRF[J]. Journal of East China University of Science and Technology (Nature Science Edition), 2019, 45, 486-490.
[20] Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality[C]//Advances in neural information processing systems. 2013: 3111-3119.
[21] Devlin J, Chang M. W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[22] Li X Y, Zhang H, Zhou X H. Chinese clinical named entity recognition with variant neural structures based on BERT methods[J]. Journal of biomedical informatics, 2020, 107: 103422.
[23] Yin X Z, Zhao H, Zhao J B, et al. Multi-neural network collaboration for Chinese military named entity recognition[J]. Journal of Tsinghua University (Science and Technology), 2020, 60(8): 648-655.
[24] Gu L, Zhang W J, Wang Y, et al. Named Entity Recognition in Judicial Field Based on BERT-BiLSTM-CRF Model[C]//In Proceedings of 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), Shanghai, China, June 2020; pp. 170–174.
[25] Nie Y Y, Tian Y H, Wan X, et al. Named Entity Recognition for Social Media Texts with Semantic Augmentation[C]//In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 2020; pp. 1383–1391.
[26] Gong C, Tang J Y, Zhou S W, et al. Chinese named entity recognition with bert[J]. DEStech Transactions on Computer Science and Engineering, 2019 (cisnrc).
[27] Wu G H, Tang G G, Wang Z R, et al. An Attention-Based BiLSTM-CRF Model for Chinese Clinic Named Entity Recognition[J]. IEEE Access 2019, 7, 113942–113949.
[28] Zhong Q, Tang Y. An Attention-Based BILSTM-CRF for Chinese Named Entity Recognition[C]//In Proceedings of 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China, April 2020; pp. 550–555.
[29] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// In Proceedings of the Advances in neural information processing systems, 2017; pp. 5998-6008.
[30] Cho K, Van M B, Gulcehre C, et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation[J]. Computer Science, 2014, 1724–1734.
[31] Bahdanau D, Cho K, Bengio Y. Neural Machine Translation by Jointly Learning to Align and Translate [J]. arXiv preprint arXiv:1409.0473, 2018.
[32] Lafferty J, Mccallum A, Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data [C]//In Proceedings of 18th International Conference on Machine Learning, 2001, 3(2):282-289.
[33] Gina A L. The third international Chinese language processing bakeoff : Word segmentation and named entity recognition[C]//Proceedings of the 5th SIGHAN Workshop on Chinese Language Proceeding, 2006: 548-554.
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