
Cross-sector generalization of accident-process role classification in occupational accident narratives
This paper evaluates cross-sector generalization of a role classifier that structures French occupational accident narratives into four accident-process roles (work situation, unfavourable condition, accident event, consequence). Classifiers trained solely on 42,244 construction-sector factual units are tested without retraining on metallurgy, chemistry-plastics, and an independent company corpus, comparing frozen pretrained representations against task-specific fine-tuning and supervised representation learning. Task-adapted strategies consistently outperformed frozen representations, reaching 85.6-85.8% average balanced accuracy across unseen sectors, supporting transferable assisted-coding systems for occupational risk analysis.
Techniques named here
- Accident-process role classification
- Cross-sector generalization evaluation
Also named here
- Transformer
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