Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures
This work addresses safety management for high-pressure gas operations, but it is incremental as it primarily introduces a new dataset without major methodological breakthroughs.
The authors tackled the problem of safety prevention in high-pressure gas operations by creating a new expert-annotated dataset called IncidentAI, which includes three NLP tasks and shows that NLP techniques are beneficial for analyzing incident reports to prevent future failures.
This paper introduces a new IncidentAI dataset for safety prevention. Different from prior corpora that usually contain a single task, our dataset comprises three tasks: named entity recognition, cause-effect extraction, and information retrieval. The dataset is annotated by domain experts who have at least six years of practical experience as high-pressure gas conservation managers. We validate the contribution of the dataset in the scenario of safety prevention. Preliminary results on the three tasks show that NLP techniques are beneficial for analyzing incident reports to prevent future failures. The dataset facilitates future research in NLP and incident management communities. The access to the dataset is also provided (the IncidentAI dataset is available at: https://github.com/Cinnamon/incident-ai-dataset).