@inproceedings{ramrakhiyani-etal-2021-extracting,
title = "Extracting Events from Industrial Incident Reports",
author = "Ramrakhiyani, Nitin and
Hingmire, Swapnil and
Patil, Sangameshwar and
Kumar, Alok and
Palshikar, Girish",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali},
booktitle = "Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.case-1.9",
doi = "10.18653/v1/2021.case-1.9",
pages = "58--67",
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.",
}
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%0 Conference Proceedings
%T Extracting Events from Industrial Incident Reports
%A Ramrakhiyani, Nitin
%A Hingmire, Swapnil
%A Patil, Sangameshwar
%A Kumar, Alok
%A Palshikar, Girish
%Y Hürriyetoğlu, Ali
%S Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F ramrakhiyani-etal-2021-extracting
%X 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.
%R 10.18653/v1/2021.case-1.9
%U https://aclanthology.org/2021.case-1.9
%U https://doi.org/10.18653/v1/2021.case-1.9
%P 58-67
Markdown (Informal)
[Extracting Events from Industrial Incident Reports](https://aclanthology.org/2021.case-1.9) (Ramrakhiyani et al., CASE 2021)
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.