Extracting Events from Industrial Incident Reports

Nitin Ramrakhiyani, Swapnil Hingmire, Sangameshwar Patil, Alok Kumar, Girish Palshikar


Abstract
Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance.
Anthology ID:
2021.case-1.9
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–67
Language:
URL:
https://aclanthology.org/2021.case-1.9
DOI:
10.18653/v1/2021.case-1.9
Bibkey:
Cite (ACL):
Nitin Ramrakhiyani, Swapnil Hingmire, Sangameshwar Patil, Alok Kumar, and Girish Palshikar. 2021. Extracting Events from Industrial Incident Reports. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 58–67, Online. Association for Computational Linguistics.
Cite (Informal):
Extracting Events from Industrial Incident Reports (Ramrakhiyani et al., CASE 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.9.pdf
Data
NomBank