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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Ziming Wei1, Hongchao Gao2, Shaocheng Qu3

  • 1Department of Electronics and Information Engineering, College of Physical Science and Technology, Central China Normal University, Wuhan, China.

Scientific Reports
|January 8, 2026
PubMed
Summary

This study introduces a new framework to improve information extraction from power equipment maintenance texts. It enhances named entity recognition (NER) for better knowledge graph construction and question-answering systems.

Keywords:
Attention mechanismDeep learningNamed entity recognitionPower system

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Power equipment maintenance work orders contain vital operational details but are challenging for automated information extraction due to complex terminology and semantics.
  • Existing methods struggle with the intricacies of power system texts, limiting automated knowledge discovery.

Purpose of the Study:

  • To propose a novel multi-level feature enhancement framework for improved information extraction from power equipment maintenance texts.
  • To address the challenges of domain-specific terminology and complex semantic structures in automated text analysis.

Main Methods:

  • Developed a Hierarchical Knowledge-Driven Data Completion method to create the Power Equipment Maintenance Named Entity Recognition (PEM-NER) dataset.
  • Integrated a Position-Aware Global Attention mechanism within a transformer architecture to capture positional and dataset-scale features.
  • Designed a Fine-Grained Information Enhancement Module to refine character-level dependency analysis for precise entity boundary detection.

Main Results:

  • The proposed framework demonstrated superior performance in recognizing entities within power system texts on the PEM-NER dataset and three public benchmarks.
  • The Position-Aware Global Attention mechanism significantly enhanced contextual understanding for NER tasks.
  • The Fine-Grained Information Enhancement Module improved the precision of entity boundary detection.

Conclusions:

  • The multi-level feature enhancement framework effectively overcomes challenges in extracting information from power equipment maintenance texts.
  • The framework shows significant promise for applications in knowledge graph construction and question-answering systems in the power equipment maintenance domain.