A Named Entity Recognition Model for Chinese Electricity Violation Descriptions Based on Word-Character Fusion and Multi-Head Attention Mechanisms

Lingwen Meng, Yulin Wang, Yuanjun Huang, Dingli Ma, Xinshan Zhu and Shumei Zhang ()
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Lingwen Meng: Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, China
Yulin Wang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Yuanjun Huang: Southern Power Grid Digital Grid Group Co., Ltd., Guizhou Branch, Guiyang 550002, China
Dingli Ma: Southern Power Grid Digital Grid Group Co., Ltd., Guizhou Branch, Guiyang 550002, China
Xinshan Zhu: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Shumei Zhang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2025, vol. 18, issue 2, 1-17

Abstract: Due to the complexity and technicality of named entity recognition (NER) in the power grid field, existing methods are ineffective at identifying specialized terms in power grid operation record texts. Therefore, this paper proposes a Chinese power violation description entity recognition model based on word-character fusion and multi-head attention mechanisms. The model first utilizes a collected power grid domain corpus to train a Word2Vec model, which produces static word vector representations. These static word vectors are then integrated with the dynamic character vector features of the input text generated by the BERT model, thereby mitigating the impact of segmentation errors on the NER model and enhancing the model’s ability to identify entity boundaries. The combined vectors are subsequently input into a BiGRU model for learning contextual features. The output from the BiGRU layer is then passed to an attention mechanism layer to obtain enhanced semantic features, which highlight key semantics and improve the model’s contextual understanding ability. Finally, the CRF layer decodes the output to generate the globally optimal label sequence with the highest probability. Experimental results on the constructed power grid field operation violation description dataset demonstrate that the proposed NER model outperforms the traditional BERT-BiLSTM-CRF model, with an average improvement of 1.58% in precision, recall, and F1-score. This demonstrates the effectiveness of the model design and further enhances the accuracy of entity recognition in the power grid domain.

Keywords: named entity recognition (NER); attention mechanism; electric grid on-site operation violation description; word-character fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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