@article{Cheng2025, 
author = {Zhibo Cheng and Yanhua Wu and Zheqian Liu and Yong Shi and Ze Li},
title = {Railway accident entity extraction method based on accident phase classification and mutual learning},
year = {2025},
journal = {Railway Sciences},
volume = {4},
number = {6},
pages = {815-832},
keywords = {Accident report texts, Entity extraction, Accident phase classification, Multi-task model, Mutual learning mechanism},
url = {https://www.sciopen.com/article/10.1108/RS-08-2025-0030},
doi = {10.1108/RS-08-2025-0030},
abstract = {PurposeThis study aims to enhance the accuracy of key entity extraction from railway accident report texts and address challenges such as complex domain-specific semantics, data sparsity and strong inter-sentence semantic dependencies. A robust entity extraction method tailored for accident texts is proposed.Design/methodology/approachThis method is implemented through a dual-branch multi-task mutual learning model named R-MLP, which jointly performs entity recognition and accident phase classification. The model leverages a shared BERT encoder to extract contextual features and incorporates a sentence span indexing module to align feature granularity. A cross-task mutual learning mechanism is also introduced to strengthen semantic representation.FindingsR-MLP effectively mitigates the impact of semantic complexity and data sparsity in domain entities and enhances the model’s ability to capture inter-sentence semantic dependencies. Experimental results show that R-MLP achieves a maximum F1-score of 0.736 in extracting six types of key railway accident entities, significantly outperforming baseline models such as RoBERTa and MacBERT.Originality/valueThis demonstrates the proposed method’s superior generalization and accuracy in domain-specific entity extraction tasks, confirming its effectiveness and practical value.}
}