A multi-level feature enhancement framework for named entity recognition in power system texts.

Ziming Wei, Hongchao Gao, Shaocheng Qu, Li Zhao, Qianqian Shi, Chen Zhang

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Ziming WeiDepartment of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.

Hongchao GaoOPT Machine Vision Tech Co., Ltd, Dongguan, China.

Shaocheng QuDepartment of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China. qushaocheng@mail.ccnu.edu.cn.

Li ZhaoDepartment of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.

Qianqian ShiDepartment of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.

Chen ZhangDepartment of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.

Funding

Key research and development program of Hubei Province 2023BAB049self-determined research funds of CCNU from the colleges'basic research and operation of MOE CCNU25ai025

6 · The paper itself

Abstract

Power equipment maintenance work orders, a critical type of power system texts, are rich in operational details such as faulty components and maintenance procedures. However, automated information extraction from these orders is impeded by complex domain-specific terminology and intricate semantic structures. This paper proposes a novel multi-level feature enhancement framework to overcome these challenges. The framework's contributions are threefold: Firstly, a Hierarchical Knowledge-Driven Data Completion method is proposed to construct the Power Equipment Maintenance Named Entity Recognition (PEM-NER) dataset, leveraging raw data from the State Grid Corporation. Secondly, a Position-Aware Global Attention mechanism is developed and integrated within the transformer architecture. This mechanism effectively captures relative positional information and dataset-scale features, significantly enhancing contextual understanding for NER tasks. Thirdly, a Fine-Grained Information Enhancement Module is designed to refine character-level dependency analysis, thereby improving the precision of entity boundary detection. Extensive evaluations on the PEM-NER dataset and three public benchmarks demonstrate the proposed model's superior performance, especially in recognizing entities within power system texts. The framework exhibits promising applications in knowledge graph construction and question-answering systems within the field of power equipment maintenance.

Indexed as

Attention mechanismDeep learningNamed entity recognitionPower system

Identifiers

PMID41507270

DOI10.1038/s41598-025-32884-0

PMCPMC12830914

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LicenceCC BY-NC-ND

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