ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives

ConstructCIE：用于从建筑事故叙述中提取因果信息的数据集

Abstract: 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.

摘要： 建筑事故叙述中包含丰富的因果信息，但这些证据往往是隐含的、跨度较长的且分布零散的。我们引入了 ConstructCIE，这是一个用于从 OSHA（美国职业安全与健康管理局）建筑事故报告中提取因果信息的人工标注数据集。该数据集采用了一种分层架构，涵盖了事故类型、因果因素、子因果因素以及支持性证据片段。

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. JHE generally achieves stronger exact and soft matching, while IHE sometimes achieves higher keyword F1.

我们在端到端的分层提取设置中评估了监督序列标注器和指令微调大语言模型（LLM）。结果表明，大多数被评估的模型在事故类型预测方面表现出色，并能恢复广泛的因果含义，但在精确的片段级提取方面仍存在局限性。JHE（联合分层提取）通常能实现更强的精确匹配和模糊匹配，而 IHE（指令分层提取）有时能获得更高的关键词 F1 分数。

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.

错误分布因提取策略而异，但证据选择错误和片段边界错误仍然很常见。这些发现表明，要实现可靠的建筑事故因果信息提取，需要更强的领域基础知识和更精确的证据提取能力。