低资源场景下的命名实体识别研究综述

doi:10.3778/j.issn.1002-8331.2411-0259

摘要/Abstract

摘要： 命名实体识别是信息抽取的关键任务之一，旨在从非结构化文本中识别出特定的实体及其类型。现有的基于机器学习和深度学习的方法通常需要大量标注数据，而实际应用中获取这些数据往往受到资源、时间和成本的限制。因此，低资源场景下的命名实体识别问题已经成为一个亟待解决的挑战。系统总结并分析了现有学术成果，明确了相关任务定义，并将其在低资源场景下划分为三类；接着深入探讨了五种主要技术路径:迁移学习、数据增强、提示学习、对比学习、元学习，分析了它们的局限性及未来改进方向；介绍了相关数据集和评价指标，并总结分析了典型技术方法的实验结果。最后，从整体上分析了当前低资源命名实体识别面临的挑战及未来研究趋势。

关键词: 命名实体识别（NER）, 低资源场景, 深度学习, 自然语言处理

Abstract: Named entity recognition is one of the key tasks of information extraction, which aims to identify specific entities and their types from unstructured text. The existing methods based on machine learning and deep learning usually require a large number of labeled data, but in practical applications, obtaining these data is often limited by resources, time and cost. Therefore, named entity recognition in low resource scenarios has become an urgent challenge. This paper systematically summarizes and analyzes the existing scholarship. Firstiy, it defines the relevant tasks and divides them into three categories in the low resource scenario. Then it discusses five main technical paths: transfer learning, data enhancement, prompt learning, comparative learning, and meta learning, and analyzes their limitations and future improvement directions. The paper introduces the relevant data sets and evaluation indicators, and summarizes and analyzes the experimental results of typical technical methods. Finally, the challenges and future research trends of low resource named entity recognition are analyzed as a whole.

Key words: named entity recognition (NER), low-resource scenarios, deep learning, natural language processing

钱丽萍, 崔雨婷, 廉露, 陈艳鹏, 黄楠楠. 低资源场景下的命名实体识别研究综述[J]. 计算机工程与应用, 2025, 61(18): 1-23.

QIAN Liping, CUI Yuting, LIAN Lu, CHEN Yanpeng, HUANG Nannan. Survey of Named Entity Recognition for Low-Resource Scenarios[J]. Computer Engineering and Applications, 2025, 61(18): 1-23.

参考文献

[1] RAU L F. Extracting company names from text[C]//Proceedings of the 7th IEEE Conference on Artificial Intelligence Application. Piscataway: IEEE, 1991: 29-32.
[2] GRISHMAN R, SUNDHEIM B. Message understanding conference-6: a brief history[C]//Proceedings of the 16th Conference on Computational Linguistics. Stroudsburg: ACL, 1996: 466-471.
[3] KRUPKA G R. SRA: description of the SRA system as used for MUC-6[C]//Proceedings of the 6th Conference on Message Understanding. Stroudsburg: ACL, 1995: 221-235.
[4] 张小衡, 王玲玲. 中文机构名称的识别与分析[J]. 中文信息学报, 1997, 11(4): 21-32.
ZHANG X H, WANG L L. Identification and analysis of Chinese organization and institution names[J]. Journal of Chinese Information Processing, 1997, 11(4): 21-32.
[5] SEKINE S, NOBATA C. Definition, dictionaries and tagger for extended named entity hierarchy[C]//Proceedings of the International Conference on Language Resources and Evaluation, 2004: 1977-1980.
[6] BIKEL D M, MILLER S, SCHWARTZ R, et al. Nymble: a high-performance learning name-finder[C]//Proceedings of the 5th Conference on Applied Natural Language Processing. Stroudsburg: ACL, 1997: 194-201.
[7] BIKEL D M, SCHWARTZ R, WEISCHEDEL R M. An algorithm that learns what’s in a name[J]. Machine Learning, 1999, 34(1): 211-231.
[8] LI W, MCCALLUM A. Rapid development of Hindi named entity recognition using conditional random fields and feature induction[J]. ACM Transactions on Asian Language Information Processing, 2003, 2(3): 290-294.
[9] CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. Cambridge, UK: Cambridge University Press, 2000.
[10] EDDY S R. Hidden Markov models[J]. Current Opinion in Structural Biology, 1996, 6(3): 361-365.
[11] KAPUR J N. Maximum-entropy models in science and engineering[M]. New York: John Wiley & Sons, 1989.
[12] LAFFERTY J D, MCCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc, 2001: 282-289.
[13] QUINLAN J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106.
[14] COLLOBERT R, WESTON J. A unified architecture for natural language processing: deep neural networks with multitask learning[C]//Proceedings of the 25th International Conference on Machine Learning. New York: ACM, 2008: 160-167.
[15] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[J]. arXiv:1508.01991, 2015.
[16] ZHAO W X, ZHOU K, LI J, et al. A survey of large language models[J]. arXiv:2303.18223, 2023.
[17] LIU L L, DING B S, BING L D, et al. MulDA: a multilingual data augmentation framework for low-resource cross-lingual NER[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2021: 5834-5846.
[18] SABANE M, RANADE A, LITAKE O, et al. Enhancing low resource NER using assisting language and transfer learning[C]//Proceedings of the 2023 2nd International Conference on Applied Artificial Intelligence and Computing. Piscataway: IEEE, 2023: 1666-1671.
[19] DAI H, LIU Z, LIAO W, HUANG X, et al. AugGPT: leveraging chatgpt for text data augmentation[J]. arXiv:2302. 13007, 2023.
[20] BAHAD S, MISHRA P, ARORA K, et al. Fine-tuning pre-trained named entity recognition models for Indian languages[J]. arXiv:2405.04829, 2024.
[21] CETOLI A, BRAGAGLIA S, O'HARNEY A D, et al. Graph convolutional networks for named entity recognition[C]//Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories. Stroudsburg: ACL, 2018: 37-45.
[22] FRITZLER A, LOGACHEVA V, KRETOV M. Few-shot classification in named entity recognition task[C]//Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. New York: ACM, 2019: 993-1000.
[23] NADEAU D, SEKINE S. A survey of named entity recognition and classification[J]. Lingvisticae Investigationes, 2007, 30(1): 3-26.
[24] YADAV V, BETHARD S. A survey on recent advances in named entity recognition from deep learning models[J]. arXiv:1910.11470, 2019
[25] LI J, SUN A X, HAN J L, et al. A survey on deep learning for named entity recognition[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(1): 50-70.
[26] 焦凯楠, 李欣, 朱容辰.中文领域命名实体识别综述[J]. 计算机工程与应用, 2021, 57(16): 1-15.
JIAO K N, LI X, ZHU Y C. Overview of Chinese Domain Named Entity Recognition[J]. Computer Engineering and Applications, 2021, 57(16): 1-15.
[27] 赵继贵, 钱育蓉, 王魁, 等.中文命名实体识别研究综述[J].计算机工程与应用, 2024, 60(1): 15-27.
ZHAO J G, QIAN Y R, WANG K, et al. Survey of Chinese named entity recognition research[J]. Computer Engineering and Applications, 2024, 60(1): 15-27.
[28] 邓依依, 邬昌兴, 魏永丰, 等. 基于深度学习的命名实体识别综述[J]. 中文信息学报, 2021, 35(9): 30-45.
DENG Y Y, WU C X, WEI Y F, et al. A survey on named entity recognition based on deep learning[J]. Journal of Chinese Information Processing, 2021, 35(9): 30-45.
[29] DENG S, ZHANG N, CHEN H, et al. Knowledge extraction in low-resource scenarios: survey and perspective[J]. arXiv:2202.08063, 2022.
[30] ZHOU R, LI X, BING L D, et al. ConNER: consistency training for cross-lingual named entity recognition[C]//Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2022: 8438-8449.
[31] 张小艳, 段正宇. 基于句级别GAN的跨语言零资源命名实体识别模型[J]. 计算机应用, 2023, 43(8): 2406-2411.
ZHANG X Y, DUAN Z Y. Cross-lingual zero-resource named entity recognition model based on sentence-level generative adversarial network[J]. Journal of Computer Applications, 2023, 43(8): 2406-2411.
[32] NI J, DINU G, FLORIAN R. Weakly supervised cross-lingual named entity recognition via effective annotation and representation projection[C]//Proceedings of the 55th Annual Meeting of the Association forComputational Linguistics. Stroudsburg: ACL, 2017: 1470-1480.
[33] MAYHEW S, TSAI C T, ROTH D. Cheap translation for cross?lingual named entity recognition[C]//Proceedings of the 2017 Conference on Empirical Methods in NaturalLanguage Processing. Stroudsburg: ACL, 2017: 2536-2545.
[34] XIE J T, YANG Z L, NEUBIG G, et al. Neural cross-lingual named entity recognition with minimal resources[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 369-379.
[35] FENG X C, FENG X C, QIN B, et al. Improving low resource named entity recognition using cross-lingual knowledge transfer[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018: 4071-4077.
[36] YANG J, HUANG S, MA S, et al. CROP: zero-shot cross-lingual named entity recognition with multilingual labeled sequence translation[J]. arXiv:2210.07022, 2022.
[37] YANG Z L, SALAKHUTDINOV R, COHEN W W, et al.Transfer learning for sequence tagging with hierarchical recurrent networks[C]//Proceedings of the 5th International Conference on Learning Representations, 2017: 1-10.
[38] WANG Z H, QU Y R, CHEN L H, et al. Label-aware double transfer learning for cross-specialty medicalnamed entity recognition[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2018: 1-15.
[39] YANG H Y, HUANG S J, DAI X Y, et al. Fine-grained knowledge fusion for sequence labeling domain adaptation[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2019: 4195-4204.
[40] LIN B Y, LU W. Neural adaptation layers for cross-domain named entity recognition[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 2012-2022.
[41] CHEN L, MOSCHITTI A. Transfer learning for sequence labeling using source model and target data[J]. arXiv:1902.05309, 2019.
[42] CAO P F, CHEN Y B, 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. Stroudsburg: ACL, 2018: 182-192.
[43] 钱泰羽, 陈一飞, 庞博文. 基于MacBERT和对抗训练的审计文本命名实体识别[J]. 计算机科学, 2023, 50(S2): 93-98.
QIAN T Y, CHEN Y F, PANG B W. Audit text named entity recognition based on MacBERT and adversarial training[J]. Computer Science, 2023, 50(S2): 93-98.
[44] PENG Q, ZHENG C M, CAI Y, et al. Unsupervised cross-domain named entity recognition using entity-aware adversarial training[J]. Neural Networks, 2021, 138: 68-77.
[45] ZHOU J T, ZHANG H, JIN D, et al. Dual adversarial neural transfer for low-resource named entity recognition[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019: 3461-3471.
[46] HU Y, ZHENG C W. 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.
[47] DAI X, ADEL H. An analysis of simple data augmentation for named entity recognition[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 3861-3867.
[48] ZHANG R Z, YU Y, ZHANG C. SeqMix: augmenting active sequence labeling via sequence mixup[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2020: 8566-8579.
[49] PHAN U, NGUYEN N. Simple semantic-based data augmentation for named entity recognition in biomedical texts[C]//Proceedings of the 21st Workshop on Biomedical Language Processing. Stroudsburg: ACL, 2022: 123-129.
[50] TORRES A E, MOURA E S, SILVA A S, et al. An experimental study on data augmentation techniques for named entity recognition on low-resource domains[J]. arXiv:2411.14551, 2024.
[51] DING B S, LIU L L, BING L D, et al. DAGA: data augmentation with a generation approach for low-resource tagging tasks[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2020: 6045-6057.
[52] CAI J, HUANG S, JIANG Y, et al. Improving low-resource named entity recognition with graph propagated data augmentation[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2023: 110-118.
[53] CHEN S G, AGUILAR G, NEVES L, et al. Data augmentation for cross?domain named entity recognition[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2021: 5346-5356.
[54] ZHOU R, LI X, HE R D, et al. MELM: data augmentation with masked entity language modeling for low-resource NER[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 2251-2262.
[55] GHOSH S, TYAGI U, SURI M N, et al. ACLM: a selective-denoising based generative data augmentation approach for low-resource complex NER[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2023: 104-125.
[56] YANG Y S, CHEN W L, LI Z H, et al. Distantly supervised NER with partial annotation learning and reinforcement learning[C]//Proceedings of the 27th International Conference on Computational Linguistics, 2018: 2159-2169.
[57] GUO R H, ROTH D. Constrained labeled data generation for low-resource named entity recognition[C]//Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg: ACL, 2021: 4519-4533.
[58] SHARMA S, JOSHI A, MUKHIJA N, et al. Systematic review of effect of data augmentation using paraphrasing on named entity recognition[C]//Proceedings of the NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, 2022.
[59] LU D, NEVES L, CARVALHO V, et al. Visual attention model for name tagging in multimodal social media[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2018: 1990-1999.
[60] MOON S, NEVES L, CARVALHO V. Multimodal named entity recognition for short social media posts[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2018: 852-860.
[61] YU J F, JIANG J, YANG L, et al. Improving multimodal named entity recognition via entity span detection with unified multimodal transformer[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2020: 3342-3352.
[62] SUN L, WANG J Q, ZHANG K, et al. RpBERT: a text-image relation propagation-based BERT model for multimodal NER[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 13860-13868.
[63] ZHENG Z H, ZHANG Z H, WANG Z X, et al. Decompose, prioritize, and eliminate: dynamically integrating diverse representations for multimodal named entity recognition[C]//Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, 2024: 4498-4508.
[64] 张天明, 张杉, 刘曦, 等. 融合多模态数据的小样本命名实体识别方法[J]. 软件学报, 2024, 35(3): 1107-1124.
ZHANG T M, ZHANG S, LIU X, et al. Multimodal data fusion for few-shot named entity recognition method[J]. Journal of Software, 2024, 35(3): 1107-1124.
[65] CUI L , WU Y , LIU J , et al. Template-based named entity recognition using BART[J]. arXiv:2106.01760, 2021.
[66] LIU A T, XIAO W, ZHU H, et al. QaNER: prompting question answering models for few-shot named entity recognition[J]. arXiv:2203.01543, 2022.
[67] SHEN Y, TAN Z, WU S, et al. PromptNER: prompt locating and typing for named entity recognition[J]. arXiv:2305. 17104, 2023.
[68] ASHOK D, LIPTON Z C. PromptNER: prompting for named entity recognition[J]. arXiv:2305.15444, 2023.
[69] LIU J, CHEN Y F, XU J N. Low-resource NER by data augmentation with prompting[C]//Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022: 4252-4258.
[70] SONG S H, SHEN F R, ZHAO J. RoPDA: robust prompt-based data augmentation for low-resource named entity recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2024 19017-19025.
[71] KANG H, SEO H, JUNG J, et al. Guidance-based prompt data augmentation in specialized domains for named entity recognition[C]//Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2024: 665-672.
[72] WANG S, SUN X, LI X, et al. GPT-NER: named entity recognition via large language models[J]. arXiv:2304.10428, 2023.
[73] DAS S S S, KATIYAR A, PASSONNEAU R, et al. CONTaiNER: few-shot named entity recognition via contrastive learning[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 6338-6353.
[74] HUANG Y C, HE K, WANG Y G, et al. COPNER: contrastive learning with prompt guiding for few-shot named entity recognition[C]//Proceedings of the International Conference on Computational Linguistics, 2022: 2515-2527.
[75] 杨三和, 赖沛超, 傅仰耿, 等. 面向中文小样本命名实体识别的BERT优化方法[J]. 小型微型计算机系统, 2025, 46(3): 602-611.
YANG S H, LAI P C, FU Y G, et al. Optimization method of BERT for Chinese few-shot named entity recognition[J]. Journal of Chinese Computer Systems, 2025, 46(3): 602-611.
[76] LI J, CHIU B, FENG S S, et al. Few-shot named entity recognition via meta-learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(9): 4245-4256.
[77] LI J, SHANG S, SHAO L. MetaNER: named entity recognition with meta-learning[C]//Proceedings of the Proceedings of The Web Conference 2020. New York: ACM, 2020: 429-440.
[78] LICHY C, GLAUDE H, CAMPBELL W. Meta-learning for few-shot named entity recognition[C]//Proceedings of the 1st Workshop on Meta Learning and Its Applications to Natural Language Processing. Stroudsburg: ACL, 2021: 44-58.
[79] MA T T, JIANG H Q, WU Q H, et al. Decomposed meta-learning for few-shot named entity recognition[C]//Proceedings of the Findings of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 1584-1596.
[80] ZHUANG F Z, QI Z Y, DUAN K Y, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2021, 109(1): 43-76.
[81] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[C]//Advances in Neural Information Processing Systems, 2014: 2672-2680.
[82] GANIN Y, USTINOVA E, AJAKAN H, et al. Domain-adversarial training of neural networks[J]. Journal of Machine Learning Research, 2017, 17(1): 2030-2096.
[83] TZENG E, HOFFMAN J, SAENKO K, et al. Adversarial discriminative domain adaptation[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 2962-2971.
[84] WEI J, ZOU K. EDA: easy data augmentation techniques for boosting performance on text classification tasks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2019: 6381-6387.
[85] MIN J, MCCOY R T, DAS D, et al. Syntactic data augmentation increases robustness to inference heuristics[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2020: 2339-2352.
[86] WANG X Y, PHAM H, DAI Z H, et al. SwitchOut: an efficient data augmentation algorithm for neural machine translation[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 856-861.
[87] BROWN T B, et al. Language models are few-shot learners[J]. arXiv:2005.14165, 2020.
[88] SCHICK T, SCHüTZE H. Exploiting cloze-questions for few-shot text classification and natural language inference[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg: ACL, 2021: 255-269.
[89] KHOSLA P, TETERWAK P, WANG C, et al. Supervised contrastive learning[C]//Proceedings of the 34th Conference on Neural Information Processing Systems, 2020: 18661-18673.
[90] MUNKHDALAI T, YU H. Meta networks[C]//Proceedings of the International Conference on Machine Learning, 2017: 2554-2563.
[91] FINN C, ABBEEL P, LEVINE S. Model-agnostic meta-learning for fast adaptation of deep networks[C]//Proceedings of the International Conference on Machine Learning, 2017: 1126-1135.
[92] BORTHWICK A, STERLING J, AGICHTEIN E, et al. NYU: description of the MENE named entity system as used in MUC-7[C]//Proceedings of the 7th Message Understanding Conference, 1998.
[93] TJONG KIM SANG E F. Introduction to the CoNLL-2002 shared task: language-independent named entity recognition[C]//Proceedings of the 6th Conference on Natural Language Learning. Stroudsburg: ACL, 2002: 1-4.
[94] ERIK F, SANG T K, MEULDER F D. Introduction to the CoNLL?2003 shared task: language-independent named entity recognition[C]//Proceedings of the 7th Conference on Natural Language Learning at HLT-NAACL 2003. Stroudsburg: ACL, 2003: 142-147.
[95] DODDINGTON G R, MITCHELL A, PRZYBOCKI M A, et al. The automatic content extraction (ACE) program-tasks, data, and evaluation[C]//Proceedings of the International Conference on Language Resources and Evaluation, 2004: 837-840.
[96] LEVOW G A. The third international Chinese language processing bakeoff: word segmentation and named entity recognition[C]//Proceedings of the International 5th SIGHAN Workshop on Chinese Language Processing, 2006: 108-117.
[97] WEISCHEDEL R, PALMER M, MARCUS M. Ontonotes release 4.0.LDC2011T03, Philadelphia, Penn: Linguistic Data Consortium[Z]. 2011.
[98] PRADHAN S, MOSCHITTI A, XUE N W, et al. Towards robust linguistic analysis using OntoNotes[C]//Proceedings of the Conference on Computational Natural Language Learning, 2013
[99] PENG N Y, DREDZE M. Named entity recognition for Chinese social media with jointly trained embeddings[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2015: 548-554.
[100] ZHANG Y, YANG J. Chinese NER using lattice LSTM[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2018: 1554-1564.
[101] XU L, DONG Q Q, YU C, et al. CLUENER2020: fine-grained name entity recognition for Chinese[J]. arXiv:2001. 04351, 2020.
[102] COLLIER N, KIM J D. Introduction to the bio-entity recognition task at JNLPBA[C]//Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications. Stroudsburg: ACL, 2004: 70-75.
[103] HU J L, SHI X, LIU Z J, et al. HITSZ_CNER: a hybrid system for entity recognition from Chinese clinical text[C]//CEUR Workshop Proceedings, 2017: 25-30.
[104] ZHANG J T, LI J Z, JIAO Z T, et al. Overview of CCKS 2018 task 1: named entity recognition in Chinese electronic medical records[C]//Proceedings of the 4th China Conference on Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. Singapore: Springer, 2019: 158-164.
[105] HAN X, WANG Z, ZHANG J, et al. Overview of the CCKS 2019 knowledge graph evaluation track: entity, relation, event and QA[J]. arXiv:2003.03875, 2020.
[106] LI Z, GAN Z, ZHANG B. Noisy label learning for Chinese medical named entity recognition based on uncertainty strategy[C]//Proceedings of the Evaluation Tasks at the China Conference on Knowledge Graph and Semantic Computing, 2020.
[107] JIE Z M, XIE P J, LU W, et al. Better modeling of incomplete annotations for named entity recognition[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2019: 729-734.
[108] ZHANG H C, HENNIG L, ALT C, et al. Bootstrapping named entity recognition in E?commerce with positive unlabeled learning[C]//Proceedings of The 3rd Workshop on E-Commerce and NLP. Stroudsburg: ACL, 2020: 1-6.
[109] LIU J J, PASUPAT P, CYPHERS S, et al. Asgard: a portable architecture for multilingual dialogue systems[C]//Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2013: 8386-8390.
[110] XU J, WEN J, SUN X, et al. A discourse-level named entity recognition and relation extraction dataset for Chinese literature text[J]. arXiv:1711.07010, 2017.
[111] WANG X R, HE S H, XIONG Z H, et al. APTNER: a specific dataset for NER missions in cyber threat intelligence field[C]//Proceedings of the 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design. Piscataway: IEEE, 2022: 1233-1238.
[112] LOUKAS L, FERGADIOTIS M, CHALKIDIS I, et al. FiNER: financial numeric entity recognition for XBRL tagging[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2022: 4419-4431.
[113] SAGURA-BEDMAR, LSBEL. SemEval-2013 task 9: extraction of drug?drug interactions from biomedical texts (DDIExtraction 2013)[C]//Proceedings of the 7th International Workshop on Semantic Evaluation, 2013: 341-350.
[114] KRALLINGER M, RABAL O, LEITNER F, et al. The CHEMDNER corpus of chemicals and drugs and its annotation principles[J]. Journal of Cheminformatics, 2015, 7(1): S2.
[115] CHEN P, WANG J, LIN H F, et al. Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning[J]. Bioinformatics, 2023, 39(8): 496.
[116] 萨仁高娃, 邬超慧, 张振, 等. 面向小样本的威胁情报命名实体识别方法[J]. 计算机工程与设计, 2024, 45(9): 2599-2605.
SA R G W, WU C H, ZHANG Z, et al. Named entity recognition method for thread intelligence for small samples[J]. Computer Engineering and Design, 2024, 45(9): 2599-2605.
[117] 王燕玲. 论命名实体识别技术在司法大数据中的适用[J]. 政法论坛, 2022, 40(5): 40-52.
WANG Y L. On the application of named entity recognition technology in judicial big data[J]. Tribune of Political Science and Law, 2022, 40(5): 40-52.
[118] 王霄, 万玉晴. 一种面向法律文书的命名实体识别方法[J]. 计算机应用与软件, 2023, 40(8): 180-186.
WANG X, WAN Y Q. A named entity identification method for legal documents[J]. Computer Applications and Software, 2023, 40(8): 180-186.
[119] 任义, 苏博, 袁帅. 教育领域下多维度特征命名实体识别方法[J]. 计算机工程, 2024, 50(10): 110-118.
REN Y, SU B, YUAN S. Multidimensional feature named entity recognition method in education domain[J]. Computer Engineering, 2024, 50(10): 110-118.