Chinese Named Entity Recognition for Bridge Damage and Defects Based on Text Mining and Natural Language Pretraining Models

作者：Jiaqi Liu, Ian Li, Fengheng Li, Xuefeng Zhao · 发表于：Journal of Construction Engineering and Management · 年份：2025 · DOI：10.1061/jcemd4.coeng-16019 · 被引用次数：5 · 研究领域：Natural Language Processing Techniques、Topic Modeling、Semantic Web and Ontologies

Bridge inspection reports are a vital source of data for bridge management and maintenance, encompassing essential structural information indispensable for damage evaluation and decision-making. However, in the process of automatically extracting unstructured textual data and identifying damage entities, because the same type of bridge damage entity often corresponds to multiple structural components, and strong correlations along with prominent nested features exist among entities, general named entity recognition (NER) methods have limited effectiveness. To address these issues, this study introduces a novel method for NER of damage and defects in bridge inspection, leveraging text mining and pretrained natural language models. First, the study constructs a specialized corpus of bridge damage and defects from a large number of bridge inspection reports, and fine-grained entity annotations are performed on sentences describing damage and defects. Next, the study proposes an advanced bridge damage entity recognition model, which integrates pretrained natural language models with deep learning models. The model leverages the Bidirectional Encoder Representations from Transformers (BERT) pretrained model to extract vector features from Chinese characters in damage-related sentences. It then utilizes a bidirectional long short-term memory (BiLSTM) network to capture sequential patterns of multitype entity labels. Finally, it integrates conditional random fields (CRF) to enforce ...