JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (5): 1-17.doi: 10.6040/j.issn.1671-9352.0.2025.177

• Review •     Next Articles

Methods of named entity recognition and applications in electric power domain

ZHANG Yong1,2, JI Wei1,3, ZHONG YI1,3   

  1. 1. Shenzhen University, College of Electronic and Information Engineering, Shenzhen 518060, Guangdong, China;
    2. Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen 518060, Guangdong, China;
    3. Southern Power Grid Digital Platform Technology(Guangdong)Co., Ltd., Shenzhen 518053, Guangdong, China
  • Published:2026-05-15

Abstract: The development of named entity recognition(NER)in the power sector has been propelled by domain-specific NER methods under the empowerment of large language model technology. In this context, the evolutionary journey of NER methods within the power domain is reviewed in this paper, where early approaches based on rules and dictionaries are introduced, followed by statistical machine learning methods. Deep learning-based models are summarized from the perspectives of the distributed embedding layer, the text encoding layer, and the label decoding layer. The application of large language models to NER tasks and their impact are also examined. Furthermore, the existing challenges currently faced by power domain NER are explored. Finally, an outlook on future research directions is presented.

Key words: domain named entity recognition, power domain, large language model, machine learning, deep learning

CLC Number: 

  • TP391
[1] 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 Yinjie, ZHANG Chengye, BAI Fengbo, et al. Review of Chinese named entity recognition research[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(2):324-341.
[2] 李猛,李艳玲,林民. 命名实体识别的迁移学习研究综述[J]. 计算机科学与探索,2021,15(2):206-218. LI Meng, LI Yanlin, LIN Min. Review of transfer learning for named entity recognition[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(2):206-218.
[3] 李新鹏,徐建航,郭子明,等. 调度自动化系统知识图谱的构建与应用[J]. 中国电力,2019, 52(2):70-77. LI Xinpeng, XU Jianhang, GUO Ziming, et al. Construction and application of knowledge graph for dispatching automation system[J]. Electric Power, 2019, 52(2):70-77.
[4] 王慧芳,曹靖,罗麟. 电力文本数据挖掘现状及挑战[J]. 浙江电力,2019,38(3):1-7. WANG Huifang, CAO Jing, LUO Lin. Current status and challenges of power text data mining[J]. Zhejiang Electric Power, 2019, 38(3):1-7.
[5] 冀振燕,孔德焱,刘伟,等. 基于深度学习的命名实体识别研究[J]. 计算机集成制造系统,2022(6):1603-1615. JI Zhenyan, KONG Deyan, LIU Wei, et al. Research on named entity recognition based on deep learning[J]. Computer Integrated Manufacturing Systems, 2022(6):1603-1615.
[6] 袁金斗,潘明明,张腾,等. 基于规则和词典的用电安全领域命名实体识别[J]. 电子技术应用,2022,48(12):22-27. YUAN Jindou, PAN Mingming, ZHANG Teng, et al. Named entity recognition in power safety domain based on rules and dictionaries[J]. Application of Electronic Technique, 2022, 48(12):22-27.
[7] 徐鹏,龚伟,宋俊典. 基于MRC的设备故障命名实体识别方法[J]. 计算机应用与软件,2024,41(5):171-176. XU Peng, GONG Wei, SONG Jundian. Named entity identification method of equipment fault based on MRC[J]. Computer Applications and Software, 2024, 41(5):171-176.
[8] 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.
[9] HUNT E B, MARIN J, STONE P J. Experiments in induction[M]. New York: Academic Press, 1966:127-156.
[10] 刘梓权,王慧芳. 基于知识图谱技术的电力设备缺陷记录检索方法[J]. 电力系统自动化,2018,42(14):158-164. LIU Ziquan, WANG Huifang. Retrieval method for power equipment defect records based on knowledge graph technology[J]. Automation of Electric Power Systems, 2018, 42(14):158-164.
[11] 孙玉芹,肖静婷,王海超. 基于多模型融合的电力运检命名实体识别[J]. 科学技术与工程,2023,23(36):15545-15552. SUN Yuqin, XIAO Jingting, WANG Haichao. Power operation and maintenance named entity recognition based on multi-model fusion[J]. Science Technology and Engineering, 2023, 23(36):15545-15552.
[12] 潘晖,赵岩,李麟,等. 电力客户需求高适配性关联抽取算法[J]. 太赫兹科学与电子信息学报,2023,21(10):1257-1262. PAN Hui, ZHAO Yan, LI Lin, et al. Highly adaptive association extraction algorithm for power customer needs[J]. Journal of Terahertz Science and Electronic Information Technology, 2023, 21(10):1257-1262.
[13] 孔静静,于琦,李敬华,等. 实体抽取综述及其在中医药领域的应用[J]. 世界科学技术-中医药现代化,2022,24(8):2957-2963. KONG Jingjing, YU Qi, LI Jinghua, et al. Review of entity extraction and its application in traditional Chinese medicine field[J]. World Science and Technology-Modernization of Traditional Chinese Medicine, 2022, 24(8):2957-2963.
[14] LAFFERTY J, MCCALLUM A, PEREIRA F C N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C] //Proceedings of the 18th International Conference on Machine Learning(ICML 2001). Massachusetts: Morgan Kaufmann Publishers, 2001:282-289.
[15] 李嘉皓,熊威,龚康,等. 融合BERT-WWM与注意力机制的电力设备缺陷实体识别研究[J]. 电力学报,2024,39(2):126-135. LI Jiahao, XIONG Wei, GONG Kang, et al. Research on entity recognition of power equipment defects integrating BERT-WWM and attention mechanism[J]. Journal of Electric Power, 2024, 39(2):126-135.
[16] 纪鑫,武同心,余婷,等. 基于多任务学习的电力文本信息抽取[J]. 北京航空航天大学学报,2024,50(8):2461-2469. JI Xin, WU Tongxin, YU Ting, et al. Power text information extraction based on multi-task learning[J]. Journal of Beihang University, 2024, 50(8):2461-2469.
[17] 田雪涵,董坤,赵剑锋,等. 基于增强优化预训练语言模型的电力数据实体识别方法[J]. 智慧电力,2024,52(6):100-107. TIAN Xuehan, DONG Kun, ZHAO Jianfeng, et al. Power data entity recognition method based on enhanced optimized pre-trained language model[J]. Smart Power, 2024, 52(6):100-107.
[18] 吴智妍,金卫,岳路,等. 电子病历命名实体识别技术研究综述[J]. 计算机工程与应用,2022,58(21):13-29. WU Zhiyan, JIN Wei, YUE Lu, et al. Review of named entity recognition technology for electronic medical records[J]. Computer Engineering and Applications, 2022, 58(21):13-29.
[19] CHURCH K W. Word2Vec[J]. Natural Language Engineering, 2017, 23(1):155-162.
[20] PENNINGTON J, SOCHER R, MANNING C D. Glove: global vectors for word representation[C] //Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP 2014). Qatar: Association for Computational Linguistics, 2014:1532-1543.
[21] 杜修明,秦佳峰,郭诗瑶,等. 电力设备典型故障案例的文本挖掘[J]. 高电压技术,2018,44(4):1078-1084. DU Xiuming, QIN Jiafeng, GUO Shiyao, et al. Text mining of typical fault cases of power equipment[J]. High Voltage Engineering, 2018, 44(4):1078-1084.
[22] 蒋逸雯,李黎,李智威,等. 基于深度语义学习的电力变压器运维文本信息挖掘方法[J]. 中国电机工程学报,2019,39(14):4162-4171. JIANG Yiwen, LI Li, LI Zhiwei, et al. Semantic learning-based text mining method for power transformer operation and maintenance[J]. Proceedings of the CSEE, 2019, 39(14):4162-4171.
[23] 李强,庄莉,赵峰,等. 基于知识增强的配电网运行信息关系抽取方法[J]. 现代电子技术,2024,47(5):171-175. LI Qiang, ZHUANG Li, ZHAO Feng, et al. Knowledge-enhanced relationship extraction method for distribution network operation information[J]. Modern Electronics Technique, 2024, 47(5):171-175.
[24] 张宇波,王有元,梁玄鸿,等. 电力设备缺陷文本的双通道语义增强网络挖掘方法[J]. 高电压技术,2024,50(5):1923-1932. ZHANG Yubo, WANG Youyuan, LIANG Xuanhong, et al. Dual-channel semantic enhancement network mining method for defect texts of power equipment[J]. High Voltage Engineering, 2024, 50(5):1923-1932.
[25] WEI Ziming, QU Shaocheng, ZHAO Li, et al. A position-and similarity-aware named entity recognition model for power equipment maintenance work orders[J]. Sensors, 2025, 25(7):2062.
[26] CONG Li, CUI Ran, DOU Zeng, et al. Named entity recognition for power data based on lexical enhancement and global pointer[C] //Proceedings of 3rd International Conference on Electronic Information Engineering, Big Data, and Computer Technology(EIBDCT 2024). California: The International Society for Optics and Photonics, 2024, v13181.
[27] ZHAO Zhenqiang, CHEN Zhenyu, LIU Jinbo, et al. Chinese named entity recognition in power domain based on Bi-LSTM-CRF[C] //Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition. Beijing: Association for Computing Machinery, 2019:176-180.
[28] JI Zhixiang, WANG Xiaohui, CAI Changyu, et al. Research on power entity recognition technology based on bidirectional long short-term memory network and conditional random field[J]. Global Energy Interconnection, 2020, 3(2):186-192.
[29] 杨秋勇,彭泽武,苏华权,等. 基于Bi-LSTM-CRF的中文电力实体识别[J]. 信息技术,2021(9):45-50. YANG Qiuyong, PENG Zewu, SU Huaquan, et al. Chinese power entity recognition based on Bi-LSTM-CRF[J]. Information Technology, 2021(9):45-50.
[30] 张大波,郭怀新,储著伟,等. 基于多分类BiLSTM-CRF的电网启动方案结构化数据转换模型研究[J]. 电力信息与通信技术,2023,21(1):54-61. ZHANG Dabo, GUO Huaixin, CHU Zhuwei, et al. Research on structured data conversion model of power grid startup scheme based on multi-class BiLSTM-CRF[J]. Electric Power Information and Communication Technology, 2023, 21(1):54-61.
[31] 肖勇,郑楷洪,王鑫,等. 基于联合神经网络学习的中文电力计量命名实体识别[J]. 浙江大学学报(理学版),2021,48(3):321-330. XIAO Yong, ZHENG Kaihong, WANG Xin, et al. Chinese power metering named entity recognition based on joint neural network learning[J]. Journal of Zhejiang University(Science Edition), 2021, 48(3):321-330.
[32] 江叶峰,孙少华,仇晨光,等. 电网故障处置预案文本中的命名实体识别研究[J]. 电力工程技术,2021,40(5):177-183. JIANG Yefeng, SUN Shaohua, QIU Chenguang, et al. Research on named entity recognition in power grid fault disposal plans[J]. Power Engineering Technology, 2021, 40(5):177-183.
[33] 徐会芳,张中浩,谈元鹏,等. 面向电网调度领域的实体识别技术[J]. 电力建设,2021,42(10):71-77. XU Huifang, ZHANG Zhonghao, TAN Yuanpeng, et al. Entity recognition technology for power grid dispatching domain[J]. Electric Power Construction, 2021, 42(10):71-77.
[34] 毛宏亮,艾孜尔古丽,陈德刚. 基于多头注意力的电网调度领域命名实体识别[J]. 计算机技术与发展,2023,33(2):181-186194. MAO Hongliang, AIZIERGULI, CHEN Degang. Named entity recognition in power grid dispatching domain based on multi-head attention[J]. Computer Technology and Development, 2023, 33(2):181-186.
[35] MENG Lingwen, WANG Yulin, BAN Guobang, et al. A multi-source embedding-based named entity recognition model for knowledge graph and its application to on-site operation violations in power grid systems[J]. Electronics 2025, 14(13):2511.
[36] 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 Chao, WANG Hanjun. GRU-based named entity recognition method for power dispatching domain[J]. Computer Systems & Applications, 2020, 29(8):185-191.
[37] 宋厚岩,王汉军. 基于GRU和PCNN的电力知识抽取[J]. 计算机系统应用,2021,30(9):200-205. SONG Houyan, WANG Hanjun. Power knowledge extraction based on GRU and PCNN[J]. Computer Systems & Applications, 2021, 30(9):200-205.
[38] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C] //Proceedings of the 2017 Conference on Advances in Neural Information Processing Systems. California: Curran Associates, 2017:5999-6009.
[39] 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 Rujia, DAI Lu, WANG Bang, et al. Review of recent research progress in Chinese named entity recognition based on deep learning[J]. Journal of Chinese Information Processing, 2022, 36(6):20-35.
[40] DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[C] //Proceedings of the Conference of the North American Chapter of the Association for Computational. Linguistics: Human Language Technologies(NAACL HLT 2019), 2019, 1:4171-4186.
[41] TAN K L, LEE C P, ANBANANTHEN K S M, et al. RoBERTa-LSTM: a hybrid model for sentiment analysis with transformer and recurrent neural network[J]. Ieee Access, 2022, 10:21517-21525.
[42] SUN Yu, WANG Shuohuan, LI Yukun, et al. ERNIE 2.0: a continual pre-training framework for language Understanding[C] //Proceedings of the 34th AAAI Conference on Artificial Intelligence(AAAI 2020). New York: Association for the Advancement of Artificial Intelligence, 2020:8968-8975.
[43] 俞阳,何玮,康雨萌. 一种面向自然语言问题的命名实体识别模型[J]. 电子设计工程,2023,31(14):29-32. YU Yang, HE Wei, KANG Yumeng. A named entity recognition model for natural language questions[J]. Electronic Design Engineering, 2023, 31(14):29-32.
[44] 顾亦然,霍建霖,杨海根,等. 基于BERT的电机领域中文命名实体识别方法[J]. 计算机工程,2021,47(8):78-83. GU Yiran, HUO Jianlin, YANG Haigen, et al. BERT-based Chinese named entity recognition method for motor domain[J]. Computer Engineering, 2021, 47(8):78-83.
[45] 刘斐,文中,吴艺. 基于BERT-BILSTM-CRF模型的电力行业事故文本智能分析[J]. 中国安全生产科学技术,2023,19(1):209-215. LIU Fei, WEN Zhong, WU Yi. Intelligent analysis of power industry accident texts based on BERT-BILSTM-CRF model[J]. Journal of Safety Science and Technology, 2023, 19(1):209-215.
[46] 黄锋,崔志美,黄志都,等. 基于知识图谱的能源互联网输电线路隐患信息检索研究[J]. 电气自动化,2023,45(3):8-10. HUANG Feng, CUI Zhimei, HUANG Zhidu, et al. Research on hidden danger information retrieval of energy internet transmission lines based on knowledge graph[J]. Electrical Automation, 2023, 45(3):8-10.
[47] 龚泽威一,肖妮,曹占国,等. 输变电设备运维知识图谱的构建及应用[J]. 电力大数据,2023,26(5):81-89. GONG Zeweiyi, XIAO Ni, CAO Zhanguo, et al. Construction and application of operation and maintenance knowledge graph for power transmission and transformation equipment[J]. Power Big Data, 2023, 26(5):81-89.
[48] FENG Jun, WANG Hongkai, PENG Liangying, et al. Chinese named entity recognition within the electric power domain[C] //International Symposium on Emerging Information Security and Applications. Singapore: Springer Nature Singapore, 2023:133-146.
[49] 孙宏云,李喜旺. 面向配电网数据的命名实体识别[J]. 计算机系统应用,2023,32(2):387-393. SUN Hongyun, LI Xiwang. Named entity recognition for distribution network data[J]. Computer Systems & Applications, 2023, 32(2):387-393.
[50] 张智源,孙水华,徐诗傲,等. 基于BERT和多窗口门控CNN的电机领域命名实体识别[J]. 计算机应用研究,2023,40(1):107-114. ZHANG Zhiyuan, SUN Shuihua, XU Shiao, et al. BERT and multi-window gated CNN-based named entity recognition for motor domain[J]. Application Research of Computers, 2023, 40(1):107-114.
[51] 蒋晨,王渊,胡俊华,等. 基于深度学习的电力实体信息识别方法[J]. 电网技术,2021,45(6):2141-2149. JIANG Chen, WANG Yuan, HU Junhua, et al. Deep learning-based power entity information recognition method[J]. Power System Technology, 2021, 45(6):2141-2149.
[52] 徐翀,王其清. 面向知识获取的电力科技领域语言模型研究[J]. 电力信息与通信技术,2023,21(4):31-36. XU Chong, WANG Qiqing. Research on language model for power science and technology domain oriented to knowledge acquisition[J]. Electric Power Information and Communication Technology, 2023, 21(4):31-36.
[53] ZHENG Kaihong, SUN Lingyun, WANG Xin, et al. Named entity recognition in electric power metering domain based on attention mechanism[J]. IEEE Access, 2021, 9:152564-152573.
[54] 杨政,蔡迪,李慧斌. 基于层次化表示的电力文本命名实体识别和匹配算法[J]. 计算机与现代化,2022(5):75-81. YANG Zheng, CAI Di, LI Huibin. Hierarchical representation-based named entity recognition and matching algorithm for power texts[J]. Journal of Computers and Modernization, 2022(5):75-81.
[55] 黄源航,强梦烨,李涛,等. 基于RoBERTa的电力领域词汇挖掘模型[J]. 电力大数据,2022,25(6):1-8. HUANG Yuanhang, QIANG Mengye, LI Tao, et al. RoBERTa-based power domain vocabulary mining model[J]. Power Big Data, 2022, 25(6):1-8.
[56] 张锐,刘剑青,张伯远,等. 基于迁移学习的电网故障处置知识图谱构建及实时辅助决策研究[J]. 电力信息与通信技术,2022,20(6):24-34. ZHANG Rui, LIU Jianqing, ZHANG Boyuan, et al. Construction of power grid fault disposal knowledge graph based on transfer learning and real-time auxiliary decision-making[J]. Electric Power Information and Communication Technology, 2022, 20(6):24-34.
[57] 王佳琪,俞灵,夏文岳,等. 基于ERNIE-IDCNN-CRF模型的电网调度领域命名实体识别方法[J]. 电力信息与通信技术,2022,20(10):1-8. WANG Jiaqi, YU Ling, XIA Wenyue, et al. ERNIE-IDCNN-CRF model-based named entity recognition method for power grid dispatching domain[J]. Electric Power Information and Communication Technology, 2022, 20(10):1-8.
[58] JI Zhixiang, WANG Xiaohui, ZHANG Jie, et al. Construction and application of power grid dispatching fault handling knowledge graph based on pre-trained model[J]. Global Energy Interconnection, 2023, 6(4):493-504.
[59] 皮俊波,齐世雄,孙文多,等. 基于UIE框架的电网故障处置预案实体和事件识别方法[J]. 中国电力,2023,56(12):138-146. PI Junbo, QI Shixiong, SUN Wenduo, et al. Entity and event recognition method for power grid fault disposal plans based on UIE framework[J]. Electric Power, 2023, 56(12):138-146.
[60] LIU Peng, SUN Zhenfu, ZHOU Biao. An ELECTRA-based model for power safety named entity recognition[J]. Applied Sciences, 2024, 14(20):9410.
[61] VINYALS O, FORTUNATO M, JAITLY N. Pointer networks[C] //Proceedings of the 2015 Conference on Advances in Neural Information Processing Systems. Montréal: Neural Information Processing Systems Foundation, 2015:2692-2700.
[62] 陈伟,杨燕. 基于指针网络的抽取生成式摘要生成模型[J]. 计算机应用,2021,41(12):3527-3533. CHEN Wei, YANG Yan. Extractive and generative summarization model based on pointer network[J]. Journal of Computer Applications, 2021, 41(12):3527-3533.
[63] 何俊,刘鹏,聂勇,等. 基于Seq2seq实体关系联合抽取的电力知识图谱构建[J]. 实验室研究与探索,2022,41(7):1-5. HE Jun, LIU Peng, NIE Yong, et al. Construction of power knowledge map based on joint extraction of Seq2seq entity relationship[J]. Research and Exploration in Laboratory, 2022, 41(7):1-5.
[64] SABOUR S, FROSST N, HINTON G E. Dynamic routing between capsules[C] //Proceedings of the 2017 Conference on Advances in Neural Information Processing Systems. California: Neural Information Processing Systems Foundation, 2017:3857-3867.
[65] 冯曙明,胡天牧,杨永成,等. 基于胶囊网络的电力供应链风险识别模型[J]. 微型电脑应用,2022,38(8):32-34. FENG Shuming, HU Tianmu, YANG Yongchegn, et al. Capsule network-based risk identification model for power supply chain[J]. Microcomputer Applications, 2022, 38(8):32-34.
[66] RADFORD A, WU J, CHILD R, et al. Language models are unsupervised multitask learners[J]. OpenAI Blog, 2019, 1(8):9.
[67] BROWN T, MANN B, RYDER N, et al. Language models are few-shot learners[J]. Advances in Neural Information Processing Systems, 2020, 33:1877-1901.
[68] WEI J, BOSMA M, ZHAO V Y, et al. Finetuned language models are zero-shot learners[C] //Proceedings of the 10th International Conference on Learning Representations(ICLR 2022). [S.l.] : OpenReview.net, 2022.
[69] LONG Ouyang, WU Jeffrey, XU Jiang, et al. Training language models to follow instructions with human feedback[C] //Proceedings of the 36th Conference on Neural Information Processing Systems(NeurIPS 2022). NeurIPS: Neural Information Processing Systems Conference, 2022, 35:27730-27744.
[70] WEI J, WANG X Z, SCHUURMANS D, et al. Chain-of-thought prompting elicits reasoning in large language models[C] //Proceedings of the 36th Conference on Neural Information Processing Systems(NeurIPS 2022). Louisiana: Curran Associates, 2022, 35:24824-24837.
[71] YAO Shunyu, YU Dian, ZHAO Jeffrey, et al. Tree of thoughts: Deliberate problem solving with large language models[C] //Proceedings of the 37th Conference on Neural Information Processing Systems(NeurIPS 2023). Louisiana: Neural Information Processing Systems Foundation, 2023, 36:11809-11822.
[72] POLAK M P, MORGAN D. Extracting accurate materials data from research papers with conversational language models and prompt engineering[J]. Nature Communications, 2024, 15(1):1569.
[73] LIU Wenjing, ZHANG Suxiang, SUN Yang, et al. New energy power domain question-method extraction and soft clustering[C] //Proceedings of the 9th International Conference on Communication and Information Processing(ICCIP 2023). New York: Association for Computing Machinery, 2023:484-491.
[74] HU Zhiqiang, LI Xinyu, PAN Xinyu, et al. A question answering system for assembly process of wind turbines based on multi-modal knowledge graph and large language model[J]. Journal of Engineering Design, 2025, 36(7/9):1093-1117.
[75] YIN Chunlin, DU Kunpeng, NONG Qiong, et al. PowerPulse: power energy chat model with LLaMA model fine-tuned on Chinese and power sector domain knowledge[J]. Expert Systems, 2024, 41(3):1-17.
[76] ZHAO Jinxiong, MA Zhicheng, ZHAO Hong, et al. Self-consistency, extract and rectify: knowledge graph enhance large language model for electric power question answering[C] //International Conference on Intelligent Computing. Singapore: Springer Nature Singapore, 2024:493-504.
[77] TANG Wei, ZHANG Yue, MAO Xun, et al. Enhanced named entity recognition and event extraction for power grid outage scheduling using a universal information extraction framework[J]. Energies, 2025, 18(14):3617.
[78] LUO Jingtang, YAO Shiying, ZHAO Changming, et al. A federated named entity recognition model with explicit relation for power grid[J]. Computers, Materials and Continua, 2023, 75(2):4207-4216.
[1] . Hate speech detection based on pre-trained models [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(3): 44-53.
[2] ZHAO Yulin, LIANG Fengning, ZHAO Teng, CAO Yaru, WANG Lin, ZHU Hong. Method for glioma gene status prediction based on deep truth discovery [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2025, 60(7): 13-21.
[3] CHEN Yunfan, WANG Yechen, WANG Long, AN Qi, FENG Zeguo. Application of SERS collaborative machine learning in biomedical detection [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2025, 60(10): 23-41.
[4] CHEN Yumin, ZHENG Guangyu, JIAO Na. Multi-label learning based on granular neural networks [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(5): 1-11.
[5] WANG Tinghua, HU Zhenwei, ZHAN Hongxiang. A novel unsupervised feature selection method [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(12): 130-140.
[6] Yiran LI,Ning ZHAO,Zhijian ZHANG. Prediction of average queue time in multi-server tandem queueing systems [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(1): 17-26.
[7] Cheng LI,Wengang CHE,Shengxiang GAO. A object detection algorithm for aerial images [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(9): 59-70.
[8] Chengcheng ZHONG,Heng ZHOU,Zitong ZHANG,Chunlei ZHANG. LAC-UNet: semantic segmentation model based on capsules for representing part-whole hierarchical features [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(11): 116-126.
[9] LI Ying, ZHANG Guo-lin. Modeling for dissolved gases concentration based on mutual information and kernel entropy component analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(7): 43-52.
[10] ZHANG Jie, PENG Guo-jun, YANG Xiu-zhang. Malicious evasion sample detection based on dynamic API call sequence and machine learning [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(7): 85-93.
[11] Fei-fei XU,Yun-jie XU. Research on matching resumes and positions based on Arc-LSTM [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2021, 56(1): 83-90.
[12] Chang-ying HAO,Yan-yan LAN,Hai-nan ZHANG,Jia-feng GUO,Jun XU,Liang PANG,Xue-qi CHENG. Dialogue generation model based on extended keywords information [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(7): 68-76.
[13] An-min ZHOU,Lei HU,Lu-ping LIU,Peng JIA,Liang LIU. Malicious Office document detection technology based on entropy time series [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(5): 1-7.
[14] LIU Biao, LU Zhe, HUANG Yu-wei, JIAO Meng, LI Quan-qi, XUE Rui. Comparative study on neural network structures in power analysis [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2019, 54(1): 60-66.
[15] PANG Bo, LIU Yuan-chao. Fusion of pointwise and deep learning methods for passage ranking [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(3): 30-35.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!