Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures

Shumpei Inoue, Minh-Tien Nguyen, Hiroki Mizokuchi, Tuan-Anh Nguyen, Huu-Hiep Nguyen, Dung Le


Abstract
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).
Anthology ID:
2023.emnlp-industry.49
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
December
Year:
2023
Address:
Singapore
Editors:
Mingxuan Wang, Imed Zitouni
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
509–521
Language:
URL:
https://aclanthology.org/2023.emnlp-industry.49
DOI:
10.18653/v1/2023.emnlp-industry.49
Bibkey:
Cite (ACL):
Shumpei Inoue, Minh-Tien Nguyen, Hiroki Mizokuchi, Tuan-Anh Nguyen, Huu-Hiep Nguyen, and Dung Le. 2023. Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 509–521, Singapore. Association for Computational Linguistics.
Cite (Informal):
Towards Safer Operations: An Expert-involved Dataset of High-Pressure Gas Incidents for Preventing Future Failures (Inoue et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-industry.49.pdf
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 https://aclanthology.org/2023.emnlp-industry.49.mp4