Few-shot model for extracting inspection report information based on bridge inspection domain-task transfer

To reduce the reliance on large-scale manually annotated samples for extracting key information of bridge inspection, a bridge inspection key information extraction model applicable to few-shot scenarios was proposed. The method comprised a bridge inspection-specific pre-trained language model, a bi-directional long short-term memory (BiLSTM) network, and a conditional random field (CRF). Through the utilization of bridge-related corpora and inspection task data for the domain pre-training and task fine-tuning, a two-stage transfer from domain knowledge to task-specific features was implemented, resulting in a pre-trained language model with high generalization for bridge-specific terminology and inspection report formats. Subsequently, the BiLSTM was employed to capture the contextual dependencies within the bridge inspection reports, while the CRF was combined to enforce structured constraints on the final extraction results. According to industry specifications and relevant research, eight key information categories commonly found in bridge inspection reports were redefined. To validate the effectiveness of the proposed approach, few-shot datasets containing only 50 and 100 sentences were utilized for training, respectively, with performance evaluated on a test set containing 1 491 sentences. Experimental results show that, when trained with 50 and 100 samples respectively, the F₁ scores of the proposed model reach 0.860 7 and 0.820 2, respectively, significantly outperforming four mainstream models. This confirms the model's superior capability in accurately extracting key information from bridge inspection reports under few-shot conditions. Moreover, ablation experiments further reveal that the two-stage transfer learning strategy effectively facilitates the rapid extraction of domain-relevant and task-discriminative features from few-shot data, thereby substantially enhancing the model's overall performance in few-shot scenarios. The proposed bridge inspection information extraction method under few-shot scenarios can be used to construct a knowledge graph for evaluating bridge structural conditions and predicting future service life.