Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250500048-6.doi: 10.11896/jsjkx.250500048

• Artificial Intelligence • Previous Articles     Next Articles

SoftLexicon-BERT-GlobalPointer-based Approach for Chinese Named Entity Recognition in High-voltage Circuit Breaker

ZHENG Mingkun, PANG Chunjiang, WANG Xinying   

  1. North China Electric Power University,School of Computer Science,Baoding,Hebei 071000,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHENG Mingkun,born in 1999,postgraduate.His main research interests include artificial intelligence and natural language processing.
    WANG Xinying,born in 1981,Ph.D candidate,lecturer.His main research interests include power vision and know-ledge graph.
  • Supported by:
    National Natural Science Foundation of China(62371188).

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Abstract: This paper presents a hybrid model based on SoftLexicon-BERT-GlobalPointer for Chinese named entity recognition(NER) in the domain of high-voltage circuit breakers,addressing challenges such as complex technical terms,fuzzy entity boundaries,and long-distance dependencies.The proposed method integrates SoftLexicon with a domain-specific lexicon to enhance word representation,leverages a BERT pre-trained model to capture contextual semantics,and applies the GlobalPointer decoding strategy to improve entity boundary detection.Experiments on a self-constructed high-voltage circuit breaker dataset show that the model achieves an F1 score of 90.62%,significantly outperforming traditional BiLSTM-CRF and baseline BERT models.This approach offers a robust and efficient NLP solution for intelligent operation and maintenance in the power equipment field.

Key words: High-voltage circuit breaker, Chinese named entity recognition, Pre-trained language model, Entity boundary recognition, Deep semantic modeling

CLC Number: 

  • TP391.1
[1] LI J,SUN A,HAN J,et al.A Survey on Deep Learning for Named Entity Recognition:Extended Abstract[C]//2023 IEEE 39th International Conference on Data Engineering(ICDE).Anaheim,CA,USA,2023:3817-3818.
[2] QUAN Y Q,ZHANG H T,YANG B,et al.A Survey of Named Entity Recognition Based on Deep Learning[J].Microelectronics & Computer,2026,43(2):8-21.
[3] SUN Z Y,CHEN L,WEI S B,et al.Chinese Named Entity Recognition Based on Boundary Information and Lexical Information Enhancement[J].Journal of Nanjing Normal University (Engineering and Technology Edition),2024,24(4):79-86.
[4] ZHANG T.Research on Chinese Medical Named Entity Recognition Based on MedBERT-wwm and GlobalPointer[D].Beijing:Beijing University of Chemical Technology,2024.
[5] LUO L,XIA Q X.Chinese NER Model Fusing ERNIE and Improved Transformer[J].Computer Technology and Development,2022,32(10):120-125.
[6] HAQUE M Z,ZAMAN S,SAURAV J R,et al.B-NER:A Novel Bangla Named Entity Recognition Dataset With Largest Entities and Its Baseline Evaluation[C]//IEEE Access.2023:45194-45205.
[7] YUAN M,LI Y.Bi-Lattice LSTM Model with Self-Attentionfor Chinese NER[C]//2020 IEEE 20th International Confe-rence on Communication Technology(ICCT).2020:1465-1470.
[8] MA R T,PENG M L,ZHANG Q,et al.Simplify the Usage of Lexicon in Chinese NER[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.2020:5951-5960.
[9] GAO Y,LI H,ZHONG W H.Chemical Safety Accident Named Entity Recognition Based on Self-attention and SoftLexicon[J].Journal of Computer Applications,2024,44(S2):61-65.
[10] CHEN C,SHI H,XU Y,et al.Text Classification of Oil Field Safety Production Hazards Based on BERT-BiLSTM[J].Science Technology and Engineering,2024,24(29):12650-12657.
[11] ZHOU L Q,LI Y,HUA Y P,et al.Entity-Relation Extraction of Oil and Gas Knowledge Graph Based on BERT-Graph-GlobalPointer[J].Electronic Components and Information Technology,2024,8(9):63-68.
[12] ZHENG X Y,CHEN T,ZHOU J J.Geographic Named Entity Extraction Model for Surface Water System Using Extended Word Embedding BERT[J].Geospatial Information,2025,23(2):1-6.
[13] WANG Y,ZHOU Y,QI H,et al.A complex history browsing text categorization method with improved BERT embedding la-yer[J/OL].https://doi.org/10.1007/s10489-025-06298.
[14] BORTHAKUR S,SARVAIYA J N.NER for Extracting Tourist Attractions from Textual Information:A BERT-BiLSTM-CRF Approach[C]//2023 IEEE Fifth International Conference on Advances in Electronics,Computers and Communications(ICAECC).Bengaluru,India,2023:1-5.
[15] ZHANG J Y.Research on Chinese Electronic Medical Record Named Entity Recognition Based on GlobalPointer and Multi-feature Fusion[D].Wuhan:Huazhong University of Science and Technology,2023.
[16] LI M J,LI W J,WANG H R.Entity Recognition Fusing Lexical Information and GlobalPointer[J].Journal of Shandong University (Engineering Science),2024,54(1):91-99.
[17] MA Y.Analysis of Software Development System Based on Natural Language Processing[J].Network Security Technology & Application,2025(3):69-72.
[18] WANG H,ZHANG C,ZHANG B,et al.SoftLexicon-BERT-BiLSTM-CRF-based Named Entity Recognition Model for Transportation Organization Design Domain[C]//2023 International Conference on Image Processing,Computer Vision and Machine Learning(ICICML).2023:203-207.
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