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Named Entity Identification in the Power Dispatch Domain Based on RoBERTa-Attention-FL Model

Author

Listed:
  • Yan Chen

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China
    Guangxi Intelligent Digital Services Research Center of Engineering Technology, Nanning 530004, China)

  • Dezhao Lin

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Qi Meng

    (Guangxi Power Grid Co., Ltd., Nanning 530022, China)

  • Zengfu Liang

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Zhixiang Tan

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

Abstract

Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa’s middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa’s last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model’s performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%.

Suggested Citation

  • Yan Chen & Dezhao Lin & Qi Meng & Zengfu Liang & Zhixiang Tan, 2023. "Named Entity Identification in the Power Dispatch Domain Based on RoBERTa-Attention-FL Model," Energies, MDPI, vol. 16(12), pages 1-13, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4654-:d:1169025
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    References listed on IDEAS

    as
    1. Lanfei He & Xuefei Zhang & Zhiwei Li & Peng Xiao & Ziming Wei & Xu Cheng & Shaocheng Qu & Manman Yuan, 2022. "A Chinese Named Entity Recognition Model of Maintenance Records for Power Primary Equipment Based on Progressive Multitype Feature Fusion," Complexity, Hindawi, vol. 2022, pages 1-11, February.
    2. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
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