Research Progress on Named Entity Recognition in Chinese Deep Learning

doi:10.3778/j.issn.1002-8331.2302-0361

Abstract

Abstract: Chinese named entity recognition（CNER） is the process of identifying and categorizing entities with specific meanings in Chinese text. It is a crucial component in many downstream tasks within natural language processing. In the past few years, deep learning technology has increasingly relied on end-to-end methods to automatically learn more complex and abstract data features, thereby reducing the need for manual annotation and addressing the issue of data sparsity in high-dimensional feature spaces. As a result, deep learning has emerged as the dominant approach for Chinese named entity recognition. This article initially provides an overview of the historical development of named entity recognition and outlines the specific challenges and intricacies associated with Chinese named entity recognition（CNER）. It then delves into the distinct processing characteristics of CNER and categorizes deep learning-based methods for CNER into three key areas：flat entity boundary problem, Chinese nested named entity recognition, and CNER small sample problem. The paper offers a detailed description of the models, subdivisions, and recent research progress in each of these areas, and presents experimental results of several noteworthy deep learning methods on relevant datasets. Finally, the article identifies the challenges and future research directions for CNER, and concludes with a summary of commonly used datasets and evaluation methods for Chinese named entity recognition.

Key words: Chinese named entity recognition, deep learning, entity boundary, Chinese nested named entity recognition, low resource Chinese named entity recognition

摘要： 中文命名实体识别（CNER）指识别中文文本中具有特定意义的实体，是自然语言处理诸多下游任务的重要基石。近年来，深度学习技术依托其端到端的方法，自动学习到更深层次和更抽象的数据特征，摆脱了人工标注的依赖，解决了高维特征空间的数据稀疏问题，从而逐渐成为中文命名实体识别方法的主流。回顾了命名实体识别的发展进程和CNER的特殊性和难点；围绕着中文命名实体识别的不同处理特点，将基于深度学习的中文命名实体识别的方法分类为扁平实体边界问题、中文嵌套命名实体识别和CNER小样本问题处理三个领域，并具体阐述这三类领域的模型、细分领域和最近的研究进展并整理了部分典型深度学习方法在相关数据集上的实验结果；再次总结了中文命名实体识别任务的常用数据集和评估方法；指出了当前中文命名实体识别技术面临的挑战和未来的研究方向。

关键词: 中文命名实体识别, 深度学习, 实体边界, 中文嵌套命名实体识别, 低资源中文命名实体识别

LI Li, XI Xuefeng, SHENG Shengli, CUI Zhiming, XU Jiabao. Research Progress on Named Entity Recognition in Chinese Deep Learning[J]. Computer Engineering and Applications, 2023, 59(24): 46-69.

李莉, 奚雪峰, 盛胜利, 崔志明, 徐家保. 深度学习中文命名实体识别研究进展[J]. 计算机工程与应用, 2023, 59(24): 46-69.

References

[1] HARMAN D.Information retrieval：the early years[J].Foundations and Trends? in Information Retrieval，2019，13（5）：425-577.
[2] KUMAR Y，KAUR K，KAUR S.Study of automatic text summarization approaches in different languages[J].Artificial Intelligence Review，2021，54（8）：5897-5929.
[3] BI M，ZHANG Q，ZUO M，et al.Bi-directional long short-term memory model with semantic positional attention for the question answering system[J].Transactions on Asian and Low-Resource Language Information Processing，2021，20（5）：1-13.
[4] WANG H，WU H，HE Z，et al.Progress in machine translation[J].Engineering，2021，18（11）.
[5] SONG Q，WU Y，LIN P，et al.Mining summaries for knowledge graph search[J].IEEE Transactions on Knowledge and Data Engineering，2018，30（10）：1887-1900.
[6] KRUPKA G.SRA：description of the SRA system as used for MUC-6[C]//Sixth Message Understanding Conference（MUC-6）：Proceedings of a Conference Held in Columbia，Maryland，November 6-8，1995.
[7] GAYEN V，SARKAR K.An HMM based named entity recognition system for Indian languages：the JU system at ICON 2013[J].arXiv：1405.7397，2014.
[8] WALLACH H M.Conditional random fields：an introduction[R].Technical Reports（CIS），2004：22.
[9] NADEAU D，SEKINE S.A survey of named entity recognition and classification[J].Lingvisticae Investigationes，2007，30（1）：3-26.
[10] 赵山，罗睿，蔡志平.中文命名实体识别综述[J].计算机科学与探索，2022，16（2）：296-304.
ZHAO S，LUO R，CAI Z P，et al.Survey of Chinese named entity recognition[J].Journal of Frontiers of Computer Science and Technology，2022，16（2）：296-304.
[11] 王颖洁，张程烨，白凤波，等.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 Y J，ZHANG C Y，BAI F B，et al.Review of Chinese named entity recognition research[J].Journal of Frontiers of Computer Science and Technology，2023，17（2）：324-341.
[12] LIU P，GUO Y，WANG F，et al.Chinese named entity recognition：the state of the art[J].Neurocomputing，2022，473：37-53.
[13] 康怡琳，孙璐冰，朱容波，等.深度学习中文命名实体识别研究综述[J].华中科技大学学报（自然科学版），2022，50（11）：44-53.
KANG Y L，SUN L B，ZHU R B，et al.Survey on Chinese named entity recognition with deep learning[J].Journal of Huazhong University of Science and Technology（Natural Science Edition），2022，50（11）：44-53.
[14] 张汝佳，代璐，王邦，等.基于深度学习的中文命名实体识别最新研究进展综述[J].中文信息学报，2022，36（6）：20-35.
ZHANG R J，DAI L，WANG B，et al.Recent advances of Chinese named entity recognition based on deep learning[J].Journal of Chinese Information Processing，2022，36（6）：20-35.
[15] QIU X，SUN T，XU Y，et al.Pre-trained models for natural language processing：a survey[J].Science China Technological Sciences，2020，63（10）：1872-1897.
[16] LUO W，YANG F.An empirical study of automatic Chinese word segmentation for spoken language understanding and named entity recognition[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，2016：238-248.
[17] CHE W，FENG Y，QIN L，et al.N-LTP：an open-source neural language technology platform for Chinese[J].arXiv：2009.11616，2020.
[18] ZHOU L，ZHANG D.NLPIR：a theoretical framework for applying natural language processing to information retrieval[J].Journal of the American Society for Information Science and Technology，2003，54（2）：115-123.
[19] QU X，ZENG J，LIU D，et al.Distantly-supervised named entity recognition with adaptive teacher learning and fine-grained student ensemble[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2023：13501-13509.
[20] PENG N，DREDZE M.Improving named entity recognition for Chinese social media with word segmentation representation learning[J].arXiv：1603.00786，2016.
[21] WU F，LIU J，WU C，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.
[22] CAO P，CHEN Y，LIU K，et al.Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing，2018：182-192.
[23] TANG X，HUANG Y，XIA M，et al.A multi-task BERT-BiLSTM-AM-CRF strategy for Chinese named entity recognition[J].Neural Processing Letters，2023，55（2）：1209-1229.
[24] ZHU P，CHENG D，YANG F，et al.ZH-NER：Chinese named entity recognition with adversarial multi-task learning and self-attentions[C]//26th International Conference on Database Systems for Advanced Applications（DASFAA 2021），Taipei，China，April 11-14，2021：603-611.
[25] WANG X，XU X，HUANG D，et al.Multi-task label-wise transformer for Chinese named entity recognition[J].ACM Transactions on Asian and Low-Resource Language Information Processing，2023，22（4）：1-15.
[26] CHEN T，XU R，HE Y，et al.Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN[J].Expert Systems with Applications，2017，72：221-230.
[27] GUI T，MA R，ZHANG Q，et al.CNN-Based Chinese NER with Lexicon Rethinking[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence，2019：4982-4988.
[28] LIU W，XU T，XU Q，et al.An encoding strategy based word-character LSTM for Chinese NER[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies（Long and Short Papers），2019：2379-2389.
[29] ZHANG Y，YANG J.Chinese NER using lattice LSTM[J].arXiv：1805.02023，2018.
[30] XUE M G，YU B W，LIU T W，et al.Porous lattice-based transformer encoder for Chinese NER[J].arXiv：1911.
02733，2019.
[31] LI X，YAN H，QIU X，et al.FLAT：Chinese NER using flat-lattice transformer[J].arXiv：2004.11795，2020.
[32] ZHU W，CHEUNG D.Lex-BERT：enhancing BERT based NER with lexicons[J].arXiv：2101.00396，2021.
[33] YAN H，DENG B，LI X，et al.TENER：adapting transformer encoder for named entity recognition[J].arXiv：1911.04474，2019.
[34] WANG B，ZHANG Z，XU K，et al.DyLex：incorporating dynamic lexicons into BERT for sequence labeling[J].arXiv：2109.08818，2021.
[35] LIU H，SONG J，PENG W，et al.TFM：a triple fusion module for integrating lexicon information in Chinese named entity recognition[J].Neural Processing Letters，2022，54（4）：3425-3442.
[36] KIPF T N，WELLING M.Semi-supervised classification with graph convolutional networks[J].arXiv：1609.02907，2016.
[37] VELI?KOVI? P，CUCURULL G，CASANOVA A，et al.Graph attention networks[J].arXiv：1710.10903，2017.
[38] LI Y，TARLOW D，BROCKSCHMIDT M，et al.Gated graph sequence neural networks[J].arXiv：1511.05493，2015.
[39] SUI D，CHEN Y，LIU K，et al.Leverage lexical knowledge 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（EMNLP-IJCNLP），2019：3830-3840.
[40] ZHAO Y，MENG K，LIU G.A multi-channel graph attention network for Chinese NER[C]//International Conference on Neural Information Processing.Cham：Springer，2021：203-214.
[41] GUI T，ZOU Y，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（EMNLP-IJCNLP），2019：1040-1050.
[42] TANG Z，WAN B，YANG L.Word-character graph convolution network for Chinese named entity recognition[J].IEEE/ACM Transactions on Audio，Speech，and Language Processing，2020，28：1520-1532.
[43] ZHU P，CHENG D，YANG F，et al.Improving Chinese named entity recognition by large-scale syntactic dependency graph[J].IEEE/ACM Transactions on Audio，Speech，and Language Processing，2022，30：979-991.
[44] ZHANG Y，GU W，MA W，et al.MCSN：multi-graph collaborative semantic network for Chinese NER[C]//International Conference on Knowledge Science，Engineering and Management.Cham：Springer，2022：322-334.
[45] WANG Y，LU L，WU Y，et al.Polymorphic graph attention network for Chinese NER[J].Expert Systems with Applications，2022：117467.
[46] XIONG Y，PENG H，XIANG Y，et al.Leveraging multi-source knowledge for Chinese clinical named entity recognition via relational graph convolutional network[J].Journal of Biomedical Informatics，2022，128：104035.
[47] MA R，PENG M，ZHANG Q，et al.Simplify the usage of lexicon in Chinese NER[J].arXiv：1908.05969，2019.
[48] ZHAO S，HU M，CAI Z，et al.Dynamic modeling cross-and self-lattice attention network for Chinese NER[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2021：14515-14523.
[49] HUANG S，SHA Y，LI R.A Chinese named entity recognition method for small-scale dataset based on lexicon and unlabeled data[J].Multimedia Tools and Applications，2022：1-22.
[50] DEVLIN J，CHANG M W，LEE K，et al.Bert：pre-training of deep bidirectional transformers for language understanding[J].arXiv：1810.04805，2018.
[51] LIU W，FU X，ZHANG Y，et al.Lexicon enhanced Chinese sequence labeling using bert adapter[J].arXiv：2105.
07148，2021.
[52] SUN M，WANG L，SHENG T，et al.Chinese named entity recognition using the improved transformer encoder and the lexicon adapter[C]//International Conference on Artificial Neural Networks.Cham：Springer，2022：197-208.
[53] GUO Q，GUO Y.Lexicon enhanced Chinese named entity recognition with pointer network[J].Neural Computing and Applications，2022，34：14535-14555.
[54] WU S，SONG X，FENG Z.Mect：multi-metadata embedding based cross-transformer for Chinese named entity recognition[J].arXiv：2107.05418，2021.
[55] ZHANG B，CAI J，ZHANG H，et al.VisPhone：Chinese named entity recognition model enhanced by visual and phonetic features[J].Information Processing & Management，2023，60（3）：103314.
[56] MAI C，LIU J，QIU M，et al.Pronounce differently，mean differently：a multi-tagging-scheme learning method for Chinese NER integrated with lexicon and phonetic features[J].Information Processing & Management，2022，59（5）：103041.
[57] LUO L，YANG Z，SONG Y，et al.Chinese clinical named entity recognition based on stroke ELMo and multi-task learning[J].Chinese Journal of Computers，2020，43（10）：1943-1957.
[58] SARZYNSKA-WAWER J，WAWER A，PAWLAK A，et al.Detecting formal thought disorder by deep contextualized word representations[J].Psychiatry Research，2021，304：114135.
[59] XUAN Z，BAO R，JIANG S.FGN：fusion glyph network for Chinese named entity recognition[C]//Knowledge Graph and Semantic Computing：Knowledge Graph and Cognitive Intelligence：5th China Conference（CCKS 2020），Nanchang，China，2021：28-40.
[60] ZHANG Z，HE D.Chinese named entity recognition method based on multi-granularity embedding[C]//2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference（ITAIC），2022：969-973.
[61] HUANG Z，RONG W，ZHANG X，et al.Token relation aware Chinese named entity recognition[J].ACM Transactions on Asian and Low-Resource Language Information Processing，2022，22（1）：1-21.
[62] 余诗媛，郭淑明，黄瑞阳，等.嵌套命名实体识别研究进展[J].计算机科学，2021，48（S2）：1-10.
YU S Y，GUO S M，HUANG R Y，et al.Overview of nested named entity recognition[J].Computer Science，2021，48（S2）：1-10.
[63] JU M，MIWA M，ANANIADOU S.A neural layered model for nested named entity recognition[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies（Volume 1：Long Papers），2018：1446-1459.
[64] 张汝佳，代璐，郭鹏，等.基于分割注意力与边界感知的中文嵌套命名实体识别算法[J].计算机科学，2023（1）：1-10.
ZHANG R J，DAI L，GUO P，et al.Chinese nested named entity recognition algorithm based on segmentation attention and boundary-aware[J].Computer Science，2023（1）：1-10.
[65] WANG J，SHOU L，CHEN K，et al.Pyramid：a layered model for nested named entity recognition[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics，2020：5918-5928.
[66] SHIBUYA T，HOVY E.Nested named entity recognition via second-best sequence learning and decoding[J].Transactions of the Association for Computational Linguistics，2020，8：605-620.
[67] 金彦亮，谢晋飞，吴迪嘉.基于分层标注的中文嵌套命名实体识别[J].上海大学学报（自然科学版），2022，28（2）：270-280.
JIN Y L，XIE J F，WU D J.Chinese nested named entity recognition based on hierarchical tagging[J].Journal of Shanghai University（Natural Science Edition），2022，28（2）：270-280.
[68] SUN L，SUN Y，JI F，et al.Joint learning of token context and span feature for span-based nested NER[J].IEEE/ACM Transactions on Audio，Speech，and Language Processing，2020，28：2720-2730.
[69] LONG X，NIU S，LI Y.Hierarchical region learning for nested named entity recognition[C]//Findings of the Association for Computational Linguistics，2020：4788-4793.
[70] SHEN Y，MA X，TAN Z，et al.Locate and label：a two-stage identifier for nested named entity recognition[J].arXiv：2105.06804，2021.
[71] HUANG P，ZHAO X，HU M，et al.Extract-select：a span selection framework for nested named entity recognition with generative adversarial training[C]//Findings of the Association for Computational Linguistics，2022：85-96.
[72] YUAN Z，TAN C，HUANG S，et al.Fusing heterogeneous factors with triaffine mechanism for nested named entity recognition[J].arXiv：2110.07480，2021.
[73] WAN J，RU D，ZHANG W，et al.Nested named entity recognition with span-level graphs[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），2022：892-903.
[74] CHEN Y，WU Y，QIN Y，et al.Recognizing nested named entity based on the neural network boundary assembling model[J].IEEE Intelligent Systems，2019，35（1）：74-81.
[75] WANG B，LU W，WANG Y，et al.A neural transition-based model for nested mention recognition[J].arXiv：1810.01808，2018.
[76] DAI X，KARIMI S，HACHEY B，et al.An effective transition-based model for discontinuous NER[J].arXiv：2004.13454，2020.
[77] WANG B，LU W.Neural segmental hypergraphs for overlapping mention recognition[J].arXiv：1810.01817，2018.
[78] LUO Y，ZHAO H.Bipartite flat-graph network for nested named entity recognition[J].arXiv：2005.00436，2020.
[79] YU J，BOHNET B，POESIO M.Named entity recognition as dependency parsing[J].arXiv：2005.07150，2020.
[80] LI J，FEI H，LIU J，et al.Unified named entity recognition as word-word relation classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2022：10965-10973.
[81] LI X，FENG J，MENG Y，et al.A unified MRC framework for named entity recognition[J].arXiv：1910.11476，2019.
[82] ZHANG S，SHEN Y，TAN Z，et al.De-bias for generative extraction in unified NER task[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics（Volume 1：Long Papers），2022：808-818.
[83] DODDINGTON G R，MITCHELL A，PRZYBOCKI M A，et al.The automatic content extraction（ACE） program-tasks，data，and evaluation[C]//Proceedings of the Fourth International Conference on Language Resources and Evaluation（LREC’04），2004.
[84] WALKER C，STRASSEL S，MEDERO J，et al.ACE 2005 multilingual training corpus[C]//LDC2006T06.Philadelphia：Linguistic Data Consortium，2006.
[85] FU G，LUKE K K.Chinese named entity recognition using lexicalized HMMs[J].ACM SIGKDD Explorations Newsletter，2005，7（1）：19-25.
[86] CHEN H H，DING Y W，TSAI S C，et al.Description of the NTU system used for MET-2[C]//Seventh Message Understanding Conference（MUC-7）：Proceedings of a Conference Held in Fairfax，Virginia，April 29-May 1，1998.
[87] XU H，LIU H，JIA Q，et al.A nested named entity recognition method for traditional Chinese medicine records[C]//International Conference on Artificial Intelligence and Security.Cham：Springer，2021：488-497.
[88] CHIANG Y L，LIN C H，SUNG C L，et al.Nested named entity recognition for Chinese electronic health records with QA-based sequence labeling[C]//Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing（ROCLING 2021），2021：18-25.
[89] MA L L，YANG J，AN B，et al.Medical named entity recognition using weakly supervised learning[J].Cognitive Computation，2022，14（3）：1068-1079.
[90] 易新河，杨鹏，文益民.中文招聘文档中专业技能词抽取的跨域迁移学习[J].数据分析与知识发现，2022，6（z1）：274-288.
YI X H，YANG P，WEN Y M.Cross-domain transfer learning for recognizing professional skills from Chinese job postings[J].Data Analysis and Knowledge Discovery，2022，6（z1）：274-288.
[91] AKBIK A，BLYTHE D，VOLLGRAF R.Contextual string embeddings for sequence labeling[C]//Proceedings of the 27th International Conference on Computational Linguistics，2018：1638-1649.
[92] PENG D L，WANG Y R，LIU C，et al.TL-NER：a transfer learning model for Chinese named entity recognition[J].Information Systems Frontiers，2020，22（6）：1291-1304.
[93] SHENG J，WUMAIER A，LI Z.POISE：efficient cross-domain Chinese named entity recognization via transfer learning[J].Symmetry，2020，12（10）：1673.
[94] YAO L，HUANG H，WANG K W，et al.Fine-grained mechanical Chinese named entity recognition based on ALBERT-AttBiLSTM-CRF and transfer learning[J].Symmetry，2020，12（12）：1986.
[95] CHEN B，GUO W，LIU Q，et al.Feature aggregation in zero-shot cross-lingual transfer using multilingual BERT[C]//2022 26th International Conference on Pattern Recognition（ICPR），2022：1428-1435.
[96] WEN G，CHEN H，LI H，et al.Cross domains adversarial learning for Chinese named entity recognition for online medical consultation[J].Journal of Biomedical Informatics，2020，112：103608.
[97] DONG X，CHOWDHURY S，QIAN L，et al.Deep learning for named entity recognition on Chinese electronic medical records：combining deep transfer learning with multitask bi-directional LSTM RNN[J].PloS One，2019，14（5）：e0216046.
[98] HU Y，ZHENG C.A double adversarial network model for multi-domain and multi-task Chinese named entity recognition[J].IEICE Transactions on Information and Systems，2020，103（7）：1744-1752.
[99] HE H，SUN X.A unified model for cross-domain and semi-supervised named entity recognition in Chinese social media[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2017，31（1）.
[100] CHEN H，YUAN S，ZHANG X.Rose-NER：robust semi-supervised named entity recognition on insufficient labeled data[C]//The 10th International Joint Conference on Knowledge Graphs，2021：38-44.
[101] ZHOU B，ZHANG Y，SONG K，et al.A span-based multimodal variational autoencoder for semi-supervised multimodal named entity recognition[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing，2022：6293-6302.
[102] HAO Z，LV D，LI Z，et al.Semi-supervised disentangled framework for transferable named entity recognition[J].Neural Networks，2021，135：127-138.
[103] ZHOU X，ZHANG X，TAO C，et al.Multi-grained knowledge distillation for named entity recognition[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，2021：5704-5716.
[104] WANG Y，HAN X，ZHOU F，et al.Distill-AER：fine-grained address entity recognition from spoken dialogue via knowledge distillation[C]//CCF International Conference on Neural Language Processing and Chinese Computing，2022：643-645.
[105] GE L，HU C M，MA G H，et al.ProKD：an unsupervised prototypical knowledge distillation network for zero-resource cross-lingual named entity recognition[J].arXiv：2301.08855，2023.
[106] LEE J，YOON W，KIM S，et al.BioBERT：a pre-trained biomedical language representation model for biomedical text mining[J].Bioinformatics，2020，36（4）：1234-1240.
[107] BIN H U，TIANYU G，GENG D，et al.Faster biomedical named entity recognition based on knowledge distillation[J].Journal of Tsinghua University （Science and Technology），2021，61（9）：936-942.
[108] CUI L，WU Y，LIU J，et al.Template-based named entity recognition using BART[J].arXiv：2106.01760，2021.
[109] MA R，ZHOU X，GUI T，et al.Template-free prompt tuning for few-shot NER[J].arXiv：2109.13532，2021.
[110] LAI P，YE F，ZHANG L，et al.PCBERT：parent and child BERT for Chinese few-shot NER[C]//Proceedings of the 29th International Conference on Computational Linguistics，2022：2199-2209.
[111] HUANG Y，HE K，WANG Y，et al.Copner：contrastive learning with prompt guiding for few-shot named entity recognition[C]//Proceedings of the 29th International Conference on Computational Linguistics，2022：2515-2527.
[112] KAN Z，FENG L，YIN Z，et al.A unified generative framework based on prompt learning for various information extraction tasks[J].arXiv：2209.11570，2022.
[113] HUANG G，ZHONG J，WANG C，et al.Prompt-based self-training framework for few-shot named entity recognition[C]//15th International Conference on Knowledge Science，Engineering and Management（KSEM 2022），Singapore，August 6-8，2022.Cham：Springer International Publishing，2022：91-103.
[114] ZHANG S，QIN Y，HOU W J，et al.Word segmentation and named entity recognition for sighan bakeoff3[C]//Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing，2006：158-161.
[115] HE H，SUN X.F-score driven max margin neural network for named entity recognition in Chinese social media[J].arXiv：1611.04234，2016.
[116] PRADHAN S，MOSCHITTI A，XUE N，et al.Towards robust linguistic analysis using ontonotes[C]//Proceedings of the Seventeenth Conference on Computational Natural Language Learning，2013：143-152.
[117] XU L，DONG Q，LIAO Y，et al.CLUENER2020：fine-grained named entity recognition dataset and benchmark for Chinese[J].arXiv：2001.04351，2020.
[118] DING R，XIE P，ZHANG X，et al.A neural multi-digraph model for Chinese NER with gazetteers[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics，2019：1462-1467.
[119] WU Q，LIN Z，KARLSSON B F，et al.Single-/multi-source cross-lingual NER via teacher-student learning on unlabeled data in target language[J].arXiv：2004.12440，2020.
[120] TANG H，LIU J，ZHAO M，et al.Progressive layered extraction（PLE）：a novel multi-task learning （MTL） model for personalized recommendations[C]//Proceedings of the 14th ACM Conference on Recommender Systems，2020：269-278.