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

[2608.06495] ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives Skip to main content Search Submit Donate Log in Computer Science > Computation and Language arXiv:2608.06495 (cs) [Submitted on 6 Aug 2026 ( v1 ), last revised 25 Aug 2026 (this version, v4)] Title: ConstructCIE: A Dataset for Extracting Causal Information from Construction Accident Narratives Authors: Hung Nguyen , Jaehoon Lee , Namgyun Kim , Kuan-Hao Huang View PDF HTML (experimental) 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. 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 this https URL . Comments: Paper accepted by EMNLP 2026 Findings Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.06495 [cs.CL] (or arXiv:2608.06495v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.06495 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hung Nguyen [ view email ] [v1] Thu, 6 Aug 2026 18:34:29 UTC (138 KB) [v2] Tue, 18 Aug 2026 22:10:46 UTC (133 KB) [v3] Sun, 23 Aug 2026 06:28:37 UTC (133 KB) [v4] Tue, 25 Aug 2026 05:03:15 UTC (133 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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