构建施工事故因果信息提取数据集，助力安全分析智能化
ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives
- 采用分层标注架构，系统化提取事故类型与因果链
- 模型能准确识别事故类别但精准定位证据片段仍有挑战
- 适合从事安全事故分析、AI辅助决策的研究者参考
施工事故报告蕴含丰富的因果信息，但证据常隐含、跨度长且分散。我们提出ConstructCIE，一个基于OSHA施工事故报告的人工标注数据集，用于因果信息抽取。该数据集采用分层结构，涵盖事故类型、因果因素、子因果因素及支持性文本片段。我们在端到端分层抽取设置下评估了监督序列标注模型与指令微调大模型。结果表明，多数模型在事故类型预测上表现良好，并能恢复整体因果语义，但在细粒度的片段级抽取上仍受限。联合分层抽取在精确匹配与软匹配上表现更优，而独立分层抽取在关键词F1上偶有更高。不同抽取策略的错误分布各异，但证据选择与边界判定错误仍普遍。研究显示，可靠地进行施工事故因果信息抽取需更强领域知识支撑与更精准的证据定位能力。代码与数据见https://github.com/lab-flair/ConstructCIE。
原文摘要 · Abstract (English)
Construction accident narratives contain rich causal information, but the evidence is often implicit, long-span, and distributed. We introduce ConstructCIE, a manually annotated dataset for Causal Information Extraction from OSHA construction accident reports. The dataset uses a hierarchical schema for accident types, causal factors, sub-causal factors, and supporting evidence spans. We evaluate supervised sequence taggers and instruction-tuned LLMs in an end-to-end hierarchical extraction setting. Results show that most evaluated models achieve strong accident-type prediction and recover broad causal meaning but remain limited in precise span-level extraction. Joint Hierarchical Extraction generally achieves stronger exact and soft matching, while Individual Hierarchical Extraction sometimes achieves higher keyword F1. Error distributions vary by extraction strategy, but evidence-selection and span-boundary errors remain common. These findings show that reliable Causal Information Extraction for construction accidents requires stronger domain grounding and more accurate evidence extraction. The code and data can be found at https://github.com/lab-flair/ConstructCIE .
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