中文命名实体识别研究综述

doi:10.3778/j.issn.1673-9418.2208028

摘要/Abstract

摘要： 随着自然语言处理领域相关技术的快速发展，作为自然语言处理的上游任务，提高命名实体识别的准确率对于后续的文本处理任务而言具有重要的意义。然而，中文和英文语系之间存在差异，导致英文的命名实体识别研究成果难以有效地迁移到中文研究中。因此从以下四方面分析了当前中文命名实体识别研究中的关键问题：首先以命名实体识别的发展历程作为主要线索，从各阶段存在的优缺点、常用方法和研究成果等角度进行了综合论述；其次从序列标注、评价指标、中文分词方法及数据集的角度出发，对中文文本预处理方法进行了总结；接着针对中文字词特征融合方法，从字融合和词融合的角度对当前的研究进行了总结，并对当前中文命名实体识别模型的优化方向进行了论述；最后分析了当前中文命名实体识别在各领域的实际应用。对当前中文命名实体识别的研究进行论述，旨在帮助科研工作者更为全面地了解该任务的研究方向和研究意义，从而为新方法和新改进的提出提供一定的参考。

关键词: 命名实体识别, 深度学习, 特征融合, 评估指标

Abstract: With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing tasks. However, due to the differences between Chinese and English languages, it is difficult to transfer the research results of English named entity recognition into Chinese research effectively. Therefore, the key issues in the current research of Chinese named entity recognition are analyzed from the following four aspects: Firstly, the development of named entity recognition is taken as the main clue, the advantages and disadvantages, common methods and research results of each stage are comprehensively discussed. Secondly, the Chinese text preprocessing methods are summarized from the perspective of sequence annotation, evaluation index, Chinese word segmentation methods and datasets. Then, aiming at the Chinese character and word feature fusion method, the current research is summarized from the perspective of character fusion and word fusion, and the optimization direction of the current Chinese named entity recognition model is discussed. Finally, the practical applications of Chinese named entity recognition in various fields are analyzed. This paper discusses the current research on Chinese named entity recognition, aiming to help researchers understand the research direction and significance of this task more comprehensively, so as to provide a certain reference for proposing new methods and new improvements.

Key words: named entity recognition, deep learning, feature fusion, evaluation metrics

王颖洁, 张程烨, 白凤波, 汪祖民, 季长清. 中文命名实体识别研究综述[J]. 计算机科学与探索, 2023, 17(2): 324-341.

WANG Yingjie, ZHANG Chengye, BAI Fengbo, WANG Zumin, JI Changqing. Review of Chinese Named Entity Recognition Research[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(2): 324-341.

参考文献

[1] GRISHMAN R, SUNDHEIM B. Message understanding conference-6: a brief history[C]//Proceedings of the 16th Conference on Computational Linguistics, Copenhagen, Aug 5-9, 1996. Stroudsburg: ACL, 1996: 466-471.
[2] FENG Y, JIANG B, WANG L, et al. Cybersecurity named entity recognition using multi-modal ensemble learning[J]. IEEE Access, 2020, 8: 63214-63224.
[3] 潘正高. 基于规则和统计相结合的中文命名实体识别研究[J]. 情报科学, 2012, 30(5): 708-712.
PAN Z G. Research on the recognition of Chinese named entity based on rules and statistics[J]. Information Science, 2012, 30(5): 708-712.
[4] 闫萍. 基于规则和概率统计相结合的中文命名实体识别研究[J]. 计算机与数字工程, 2011, 39(9): 88-91.
YAN P. Research on the indentifiction for Chinese named entity based on combination of rules and statistic analysis[J]. Computer and Digital Engineering, 2011, 39(9): 88-91.
[5] RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition[J]. Proceedings of the IEEE, 1989, 77(2): 257-286.
[6] LAFFERTY J D, MCCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning, Williamstown, Jun 28-Jul 1, 2001. San Mateo: Morgan Kaufmann, 2001: 282-289.
[7] 王欢, 朱文球, 吴岳忠, 等. 基于数控机床设备故障领域的命名实体识别[J]. 工程科学学报, 2020, 42(4): 476-482.
WANG H, ZHU W Q, WU Y Z, et al. Named entity recognition based on equipment and fault field of CNC machine tools[J]. Chinese Journal of Engineering, 2020, 42(4): 476-482.
[8] 杨培, 杨志豪, 罗凌, 等. 基于注意机制的化学药物命名实体识别[J]. 计算机研究与发展, 2018, 55(7): 1548-1556.
YANG P, YANG Z H, LUO L, et al. An attention-based approach for chemical compound and drug named entity recog-nition[J]. Journal of Computer Research and Development, 2018, 55(7): 1548-1556.
[9] 李健, 熊琦, 胡雅婷, 等. 基于Transformer和隐马尔科夫模型的中文命名实体识别方法[J]. 吉林大学学报(工学版), 2021. DOI: 10.13229/j.cnki.jdxbgxb20210856.
LI J, XIONG Q, HU Y T, et al. Chinese named entity recognition method based on transformer and hidden Markov model[J]. Journal of Jilin University (Engineering and Technology Edition), 2021. DOI: 10.13229/j.cnki.jdxbgxb20210856.
[10] ALNABKI M W, EDUARDO F, et al. Improving named entity recognition in noisy user-generated text with local distance neighbor feature[J]. Neurocomputing, 2020, 382(C): 1-11.
[11] ZHAO Z H, YANG Z H, LUO L, et al. Disease named entity recognition from biomedical literature using a novel convolutional neural network[J]. BMC Medical Genomics, 2017, 10(S5): 73.
[12] 王蓬辉, 李明正, 李思. 基于数据增强的中文医疗命名实体识别[J]. 北京邮电大学学报, 2020, 43(5): 84-90.
WANG P H, LI M Z, LI S. Data augmentation for Chinese clinical named entity recognition[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 84-90.
[13] AGUILAR G, MAHARJAN S, SOLORIO T, et al. A multi-task approach for named entity recognition in social media data[J]. arXiv:1906.04135, 2019.
[14] 郭旭超, 唐詹, 刁磊, 等. 基于部首嵌入和注意力机制的病虫害命名实体识别[J]. 农业机械学报, 2020, 51(S2): 335-343.
GUO X C, TANG Z, DIAO L, et al. Recognition of Chinese agricultural diseases and pests named entity with joint radical-embedding and self-attention mechanism[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(S2): 335-343.
[15] 张晗, 郭渊博, 李涛. 结合GAN与BiLSTM-Attention-CRF的领域命名实体识别[J]. 计算机研究与发展, 2019, 56(9): 1851-1858.
ZHANG H, GUO Y B, LI T. Domain named entity recognition combining GAN and BiLSTM-attention-CRF[J]. Journal of Computer Research and Development, 2019, 56(9): 1851-1858.
[16] DAS P, DAS K A. A graph based clustering approach for relation extraction from crime data[J]. IEEE Access, 2019, 7: 101269-101282.
[17] 赵鹏飞, 赵春江, 吴华瑞, 等. 基于注意力机制的农业文本命名实体识别[J]. 农业机械学报, 2021, 52(1): 185-192.
ZHAO P F, ZHAO C J, WU H R, et al. Named entity recognition of Chinese agricultural text based on attention mechanism[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(1): 185-192.
[18] 杨超男, 彭敦陆. 融合BSRU和胶囊网络的文档级实体关系抽取模型[J]. 小型微型计算机系统, 2022, 43(5): 964-968.
YANG C N, PENG D L. Document-level entity relation extraction method integrating bidirectional simple recurrent unit and capsule network[J]. Journal of Chinese Computer Systems, 2022, 43(5): 964-968.
[19] 郭晓然, 罗平, 王维兰. 基于Transformer编码器的中文命名实体识别[J]. 吉林大学学报(工学版), 2021, 51(3): 989-995.
GUO X R, LUO P, WANG W L. Chinese named entity recognition based on transformer encoder[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(3): 989-995.
[20] 蔡庆. 多准则融合的中文命名实体识别方法[J]. 东南大学学报(自然科学版), 2020, 50(5): 929-934.
CAI Q. Chinese named entity recognition based on multi-criteria fusion[J]. Journal of Southeast University (Natural Science Edition), 2020, 50(5): 929-934.
[21] LIU J G, XIA C H. Innovative deep neural network modeling for fine-grained Chinese entity recognition[J]. Electronics, 2020, 9(6): 1001.
[22] 陈剑, 何涛, 闻英友, 等. 基于BERT模型的司法文书实体识别方法[J]. 东北大学学报(自然科学版), 2020, 41(10): 1382-1387.
CHEN J, HE T, WEN Y Y. Entity recognition method for judical documents based on BERT model[J]. Journal of Northeastern University (Natural Science), 2020, 41(10): 1382-1387.
[23] DUAN H, SUI Z, GE T. The CIPS-SIGHAN CLP 2014 Chinese word segmentation bake-off[C]//Proceedings of the 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, Wuhan, Oct 20-21, 2014. Stroudsburg: ACL, 2014: 90-95.
[24] XIANG L, LI X Q, ZHOU Y. Word segmenter for Chinese micro-blogging text segmentation report for CIPS-SIGHAN 2014 bakeoff[C]//Proceedings of the 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, Wuhan, Oct 20-21, 2014. Stroudsburg: ACL, 2014: 96-100.
[25] TANG J, WU Q, LI Y H. An optimization algorithm of Chinese word segmentation based on dictionary[C]//Proceedings of the 2015 International Conference on Network and Information Systems for Computers, Wuhan, Jan 23-25, 2015. Piscataway: IEEE, 2015: 259-262.
[26] SHU X, WANG J, SHEN X, et al. Word segmentation in Chinese language processing[J]. Statistics & Its Interface, 2017, 10(2): 165-173.
[27] XUE N W. Chinese word segmentation as character tagging[J]. International Journal of Computational Linguistics & Chinese Language Processing, 2003, 8(1): 29-47.
[28] SONG B C, CHAI B, ZHANG Q, et al. A Chinese word segment model for energy literature based on neural networks with electricity user dictionary[C]//Proceedings of the 2019 International Conference on Asian Language Processing, Shanghai, Nov 15-17, 2019. Piscataway: IEEE, 2019: 194-198.
[29] BOWMAN S, ZHU X D. Deep learning for natural language inference[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Jun 2, 2019. Stroudsburg: ACL, 2019: 6-8.
[30] MENG Y, SHEN J M, ZHANG C, et al. Weakly-supervised neural text classification[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 983-992.
[31] WANG M, CAI Q, WANG L Y, et al. Chinese news text classification based on attention-based CNN-BiLSTM[C]//Proceedings of the 11th International Symposium on Multispectral Image Processing and Pattern Recognition, Wuhan, Nov 2-3, 2019. Bellingham: SPIE, 2020: 12-20.
[32] LI L X, GONG P, JI L K, et al. A deep attention network for Chinese word segment[C]//Proceedings of the 2019 11th International Conference on Machine Learning and Computing, Zhuhai, Feb 22-24, 2019. New York: ACM, 2019: 528-532.
[33] ZHANG M S, ZHANG Y, CHE W X, et al. Type-supervised domain adaptation for joint segmentation and POS-tagging[C]//Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Apr 26-30, 2014. Stroudsburg: ACL, 2014: 588-597.
[34] QIU L K, ZHANG Y. Word segmentation for Chinese novels[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Jan 25-30, 2015. New York: ACM, 2015: 2440-2446.
[35] ZHANG Q, LIU X Y, FU J L. Neural networks incorporating dictionaries for Chinese word segmentation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 5682-5689.
[36] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, Dec 4-9, 2017. Red Hook: Curran Associates , 2017: 6000-6010.
[37] TANG G B, MüLLER M, RIOS A, et al. Why self-attention? A targeted evaluation of neural machine translation architectures[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Oct 31-Nov 4, 2018. Stroudsburg: ACL, 2018: 4263-4272.
[38] KITAEV N, KLEIN D. Constituency parsing with a self-attentive encoder[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Jul 15-20, 2018. Stroudsburg: ACL, 2018: 2676-2686.
[39] TAN Z X, WANG M X, XIE J, et al. Deep semantic role labeling with self-attention[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 4929-4936.
[40] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Jun 2-7, 2019. Stroudsburg: ACL, 2019: 4171-4186.
[41] RANFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative pre-training[J]. Computation and Language, 2017, 4(6): 212-220.
[42] AL-RFOU R, CHOE D, CONSTANT N, et al. Character-level language modeling with deeper self-attention[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 3159-3166.
[43] GAN L L, ZHANG Y. Investigating self-attention network for Chinese word segmentation[J]. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020, 28: 2933-2941.
[44] LI H B, HAGIWARA M, LI Q, et al. Comparison of the impact of word segmentation on name tagging for Chinese and Japanese[C]//Proceedings of the 9th Edition of the Language Resources and Evaluation Conference, Reykjavik, May 26-31, 2014. Paris: European Language Resources Association, 2014: 2532-2536.
[45] ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Jul 15-20, 2018. Stroudsburg: ACL, 2018: 1554-1564.
[46] SUI D B, CHEN Y B, LIU K, et al. Leverage lexical know-ledge for Chinese named entity recognition via collaborative graph network[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 3830-3840.
[47] GUI T, ZOU Y C, ZHANG Q, et al. A lexicon-based graph neural network for Chinese NER[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 1040-1050.
[48] MA R, GUI T, ZHANG Q, et al. CNN-based Chinese NER with lexicon rethinking[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019. Menlo Park: AAAI, 2019: 4982-4988.
[49] KONG B, LIU S, WEI F, et al. Chinese relation extraction using extend softword[J]. IEEE Access, 2021, 9: 110299 - 110308.
[50] LI X N, YAN H, QIU X P, et al. FLAT: Chinese NER using flat-lattice transformer[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Washington, Jul 5-10, 2020. Stroudsburg: ACL, 2020: 6836-6842.
[51] ZHANG Y, LIU Y G, ZHU J J, et al. Learning Chinese word embeddings from stroke, structure and pinyin of characters[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 1011-1020.
[52] LI Y, NING H. Multi-feature keyword extraction method based on TF-IDF and Chinese grammar analysis[C]//Proceedings of the 2021 International Conference on Machine Learning and Intelligent Systems Engineering, Chongqing, Jul 9-11, 2021. Piscataway: IEEE, 2021: 362-365.
[53] LIU F, LU H, LO C, et al. Learning character-level compositionality with visual features[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, Jul 30-Aug 4, 2017. Stroudsburg: ACL, 2017: 2059-2068.
[54] SU T R, LEE H Y, Learning Chinese word representations from glyphs of characters[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Sep 7-11, 2017. Stroudsburg: ACL, 2017: 264-273.
[55] MENG Y X, WU W, WANG F, et al. Glyce: glyph-vectors for Chinese character representations[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Dec 8-14, 2019. New York: ACM, 2019: 2746-2757.
[56] CAO S S, LU W, ZHOU J, et al. Cw2vec: learning Chinese word embeddings with stroke n-gram information[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, New Orleans, Feb 2-7, 2018. Menlo Park: AAAI, 2018: 5053-5061.
[57] ZHANG L T, KOMACHI M. Chinese-Japanese unsupervised neural machine translation using sub-character level information[J]. arXiv:1903.00149, 2019.
[58] SUN Y M, LIN L, YANG N, et al. Radical-enhanced Chinese character embedding[C]//LNCS 8835: Proceedings of the 21st International Conference on Neural Information Processing, Kuching, Nov 3-6, 2014. Cham: Springer, 2014: 279-286.
[59] SHAO Y, HARDMEIER C, TIEDEMANN J, et al. Character-based joint segmentation and pos tagging for Chinese using bidirectional RNN-CRF[C]//Proceedings of the 8th International Joint Conference on Natural Language Processing, Taipei, China, Nov 27-Dec 1, 2017. Stroudsburg: ACL, 2017: 173-183.
[60] CHEN A G, YIN C L. CRW-NER: exploiting multiple embeddings for Chinese named entity recognition[C]//Proceedings of the 2021 4th International Conference on Artificial Intelligence and Big Data, Chengdu, May 28-31, 2021. Piscataway: IEEE, 2021: 520-524.
[61] YANG J, WANG H M, TANG Y T, et al. Incorporating lexicon and character glyph and morphological features into BiLSTM-CRF for Chinese medical NER[C]//Proceedings of the 2021 IEEE International Conference on Consumer Electronics and Computer Engineering, Guangzhou, Jan 15-17, 2021. Piscataway: IEEE, 2021: 12-17.
[62] ZHU W H, JIN X. Improve word embedding using both writing and pronunciation[J]. PLoS One, 2018, 13(12): e0208785.
[63] CHAUDHARY A, ZHOU C T, LEVIN L, et al. Adapting word embeddings to new languages with morphological and phonological subword representations[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Oct 31-Nov 4, 2018. Stroudsburg: ACL, 2018: 3285-3295.
[64] ZHANG Y, LIU Y G, ZHU J J, et al. FSPRM: a feature subsequence based probability representation model for Chinese word embedding[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021, 29: 1702-1716.
[65] SUTHAHARAN S. Support vector machine[M]. Berlin, Heidelberg: Springer, 2016.
[66] PETERS M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations[J]. arXiv:1802.05365, 2018.
[67] YADA V V, BETHARD S. A survey on recent advances in named entity recognition from deep learning models[J]. arXiv:1910.11470, 2019.
[68] YOHANNES H M, AMAGASA T. Named-entity recognition for a low-resource language using pre-trained language model[C]//Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, Apr 25-29, 2022. New York: ACM, 2022: 837-844.
[69] XIE Y L, LI A P, CHONG C F. Research on effects of pre-trained language models in medical named entity recognition[C]//Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City, Guangzhou, Dec 22-25, 2021. New York: ACM, 2021: 148-153.
[70] LEE L H, LU Y. Multiple embeddings enhanced multi-graph neural networks for Chinese healthcare named entity recognition[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(7): 2801-2810.
[71] WEI J Q, REN X Z, LI X G, et al. NeZha: neural contextualized representation for Chinese language understanding[J]. arXiv:1909.00204, 2019.
[72] LI S, BAO Z Q, ZHAO S, et al. A LEBERT-based model for named entity recognition[C]//Proceedings of the 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture, Manchester, Oct 23-25, 2021. New York: ACM, 2021: 980-983.
[73] LAN Z Z, CHEN M D, GOODMAN S, et al. ALBERT: a lite BERT for self-supervised learning of language representations[J]. arXiv:1909.11942, 2019.
[74] XIONG Z Q, KONG D Z, XIA Z C, et al. Chinese government official document named entity recognition based on Albert[C]//Proceedings of the 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics, Chengdu, Apr 24-26, 2021. Piscataway: IEEE, 2021: 350-354.
[75] WEN S, ZENG B, LIAO W X. Named entity recognition for instructions of Chinese medicine based on pre-trained language model[C]//Proceedings of the 2021 3rd International Conference on Natural Language Processing, Beijing, Mar 26-28, 2021. Piscataway: IEEE, 2021: 139-144.
[76] XIAO Y L, ZHAO Q, LI J Q, et al. MLNER: exploiting multi-source lexicon information fusion for named entity recognition in Chinese medical text[C]//Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference, Madrid, Jul 12-16, 2021. Piscataway: IEEE, 2021: 1079-1084.
[77] ZHANG Z C, QIN X H, QIU Y L, et al. Well-behaved transformer for Chinese medical NER[C]//Proceedings of the 2021 3rd International Conference on Natural Language Processing, Beijing, Mar 26-28, 2021. Piscataway: IEEE, 2021: 162-167.
[78] ZHU Z C, LI J Q, ZHAO Q, et al. Medical named entity recognition of Chinese electronic medical records based on stacked bidirectional long short-term memory[C]//Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference, Madrid, Jul 12-16, 2021. Piscataway: IEEE, 2021: 1930-1935.
[79] CHEN H Y, YUAN S W, ZHANG X. ROSE-NER: robust semi-supervised named entity recognition on insufficient labeled data[C]//Proceedings of the 10th International Joint Conference on Knowledge Graphs, Dec 6-8, 2021. New York: ACM, 2021: 38-44.
[80] LIU Y H, OTT M, GOYAL N, et al. RoBERTa: a robustly optimized BERT pretraining approach[J]. arXiv:1907.11692, 2019.
[81] LI Z M, YUN H Y, GUO Z B, et al. Medical named entity recognition based on multi-feature fusion of BERT[C]//Proceedings of the 2021 4th International Conference on Big Data Technologies, Zibo, Sep 24-26, 2021. New York: ACM, 2021: 86-91.
[82] ZHENG H Y, QIN B, XU M. Chinese medical named entity recognition using CRF-MT-Adapt and NER-MRC[C]//Proceedings of the 2021 2nd International Conference on Computing and Data Science, Stanford, Jan 28-29, 2021. Piscataway: IEEE, 2021: 362-365.
[83] BROWN T B, MANN B, RYDER N, et al. Language models are few-shot learners[C]//Proceedings of the Annual Conference on Neural Information Processing Systems 2020, Vancouver, Dec 6-12, 2020: 1877-1901.
[84] BINA N, HAN X P, CHEN B, et al. Benchmarking knowledge-enhanced common sense question answering via knowledge-to-text transformation[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, 33rd Conference on Innovative Applications of Artificial Intelligence, the 11th Symposium on Educational Advances in Artificial Intelligence, Feb 2-9, 2021. Menlo Park: AAAI, 2021: 12574-12582.
[85] PETRONI F, LEWIS P, PIKTUS A, et al. How context affects language models?? factual predictions[J]. arXiv:2005.04611, 2020.
[86] GAO T Y, FISCH A, CHEN D Q. Making pre-trained language models better few-shot learners[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Aug 1-6, 2021. Stroudsburg: ACL, 2021: 3816-3830.
[87] CAO B X, LIN H Y, HAN X P, et al. Knowledgeable or educated guess? Revisiting language models as knowledge bases[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Aug 1-6, 2021. Stroudsburg: ACL, 2021: 1860-1874.
[88] ZHAO T Z, WALLACE E, FENG S, et al. Calibrate before use: improving few-shot performance of language models[J]. arXiv:2102.09690, 2021.
[89] MIN S, LYU X X, HOLTZMAN A, et al. Rethinking the role of demonstrations: what makes in-context learning work?[J]. arXiv:2202.12837, 2022.
[90] 苏嘉, 何彬, 吴昊, 等. 基于中文电子病历的心血管疾病风险因素标注体系及语料库构建[J]. 自动化学报, 2019, 45(2): 420-426.
SU J, HE B, WU H, et al. Annotation scheme and corpus construction for cardiovascular diseases risk factors from Chinese electronic medical records[J]. Acta Automatica Sinica, 2019, 45(2): 420-426.
[91] 罗熹, 夏先运, 安莹, 等. 结合多头自注意力机制与BiLSTM-CRF的中文临床实体识别[J]. 湖南大学学报(自然科学版), 2021, 48(4): 45-55.
LUO X, XIA X Y, AN Y, et al. Chinese CNER combined with multi-head self-attention and BiLSTM-CRF[J]. Journal of Hunan University (Natural Sciences), 2021, 48(4): 45-55.
[92] WANG Y, SUN Y N, MA Z C, et al. A hybrid model for named entity recognition on Chinese electronic medical records[J]. ACM Transactions on Asian and Low-Resource Language Information Processing, 2021, 20(2): 1-12.
[93] TIAN S B, ERDENGASILENG A, YANG X, et al. Transformer-based named entity recognition for parsing clinical trial eligibility criteria[C]//Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Gainesville, Aug 1-4, 2021. New York: ACM, 2021: 1-6.
[94] 张芳丛, 秦秋莉, 姜勇, 等. 基于RoBERTa-WWM-BiLSTM-CRF的中文电子病历命名实体识别研究[J]. 数据分析与知识发现, 2022, 6(Z1): 251-262.
ZHANG F C, QIN C L, JIANG Y, et al. Named entity recog-nition for Chinese EMR with RoBERTa-WWM-BiLSTM-CRF[J]. Data Analysis and Knowledge Discovery, 2022, 6(Z1): 251-262.
[95] 景慎旗, 赵又霖. 面向中文电子病历文书的医学命名实体识别研究——一种基于半监督深度学习的方法[J]. 信息资源管理学报, 2021, 11(6): 105-115.
JING S Q, ZHAO Y L. Recognizing clinical named entity from Chinese electronic medical record texts based on semi-supervised deep learning[J]. Journal of Information Resources Management, 2021, 11(6): 105-115.
[96] 李春楠, 王雷, 林鸿飞, 等. 基于BERT的盗窃罪法律文书命名实体识别方法[J]. 中文信息学报, 2021, 35(8): 73-81.
LI C N, WANG L, LIN H F, et al. BERT based named entity recognition for legal texts on theft cases[J]. Journal of Chinese Information Processing, 2021, 35(8): 73-81.
[97] LIU J X, YE L, ZHANG H L, et al. Named entity recognition of legal judgment based on small-scale labeled data[C]//Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies, Guangzhou, Dec 4-6, 2020. New York: ACM, 2020: 549-555.
[98] 丁家伟, 刘晓栋. 基于ELECTRA-CRF的电信网络诈骗案件文本命名实体识别模型[J]. 信息网络安全, 2021, 21(6): 63-69.
DING J W, LIU X D. Named entity recognition model of telecommunication network fraud crime based on ELECTRA-CRF[J]. Netinfo Security, 2021, 21(6): 63-69.
[99] ROEGIEST A, HDUEK A K, MCNULTY A. A dataset and an examination of identifying passages for due diligence[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, Jul 8-12, 2018. New York: ACM, 2018: 465-474.
[100] DONNELLY J, ROEGIEST A. The utility of context when extracting entities from legal documents[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management, Ireland, Oct 19-23, 2020. New York: ACM, 2020: 2397-2404.