Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling
There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/10/837 |
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| author | Yaxi Xu Zurui Gan Rengang Guo Xin Wang Ke Shi Pengfei Ma |
| author_facet | Yaxi Xu Zurui Gan Rengang Guo Xin Wang Ke Shi Pengfei Ma |
| author_sort | Yaxi Xu |
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| description | There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information. |
| format | Article |
| id | doaj-art-0ca5b687d85c4c4891c3a285bf90780d |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-0ca5b687d85c4c4891c3a285bf90780d2025-08-20T02:11:01ZengMDPI AGAerospace2226-43102024-10-01111083710.3390/aerospace11100837Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic ModelingYaxi Xu0Zurui Gan1Rengang Guo2Xin Wang3Ke Shi4Pengfei Ma5School of Economics and Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaCAAC Academy, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaCAAC Academy, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, ChinaThere are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information.https://www.mdpi.com/2226-4310/11/10/837aviation safetysafety oversightpre-trained language modeltext classificationtopic modeling |
| spellingShingle | Yaxi Xu Zurui Gan Rengang Guo Xin Wang Ke Shi Pengfei Ma Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling Aerospace aviation safety safety oversight pre-trained language model text classification topic modeling |
| title | Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling |
| title_full | Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling |
| title_fullStr | Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling |
| title_full_unstemmed | Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling |
| title_short | Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling |
| title_sort | hazard analysis for massive civil aviation safety oversight reports using text classification and topic modeling |
| topic | aviation safety safety oversight pre-trained language model text classification topic modeling |
| url | https://www.mdpi.com/2226-4310/11/10/837 |
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