A Named Entity Recognition Method for GIS Defect Texts Incorporating an Engineering Format-Aware Masking Strategy

Abstract

Named entity recognition (NER) is a key technique for extracting entities such as equipment, components and defect types from GIS defect texts, providing a basis for subsequent knowledge graph construction. However, GIS defect texts contain many engineering structures, including engineering abbreviations, equipment numbers and phase identifiers, making it difficult for general-purpose models to stably recognize their semantic associations and entity boundaries. To address this problem, this paper proposes an engineering format-aware masking strategy. The strategy identifies candidate fragments using format rules for phase identifiers, measurement value-unit patterns and engineering abbreviations and preferentially selects them as perturbation targets to strengthen the model’s understanding of engineering structures and their contextual relationships. Bidirectional long short-term memory is used to extract bidirectional sequence features, and a conditional random field is used to model transition constraints between labels and obtain the globally optimal label sequence. The results show that the proposed model achieves precision, recall and F1 scores of 0.89, 0.92 and 0.90, respectively. The analysis indicates that the proposed method improves entity recognition for phase-related structures, engineering abbreviations and equipment hierarchy fragments.