Version 1
: Received: 8 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (11:19:10 CEST)
How to cite:
Chandra, C.; Ojima, Y.; Bendarkar, M. V.; Mavris, D. N. Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports. Preprints2024, 2024100656. https://doi.org/10.20944/preprints202410.0656.v1
Chandra, C.; Ojima, Y.; Bendarkar, M. V.; Mavris, D. N. Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports. Preprints 2024, 2024100656. https://doi.org/10.20944/preprints202410.0656.v1
Chandra, C.; Ojima, Y.; Bendarkar, M. V.; Mavris, D. N. Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports. Preprints2024, 2024100656. https://doi.org/10.20944/preprints202410.0656.v1
APA Style
Chandra, C., Ojima, Y., Bendarkar, M. V., & Mavris, D. N. (2024). Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports. Preprints. https://doi.org/10.20944/preprints202410.0656.v1
Chicago/Turabian Style
Chandra, C., Mayank V. Bendarkar and Dimitri N. Mavris. 2024 "Aviation-BERT-NER: Named Entity Recognition for Aviation Safety Reports" Preprints. https://doi.org/10.20944/preprints202410.0656.v1
Abstract
This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology's Aerospace Systems Design Laboratory. Unlike general NER models, this system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to enhance its precision in identifying and classifying named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirrors the complexity of real-world aviation safety reports. This method significantly improves the model's generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Fine-tuning of the Aviation-BERT model demonstrated strong performance in precision, recall, and F1 scores, showing its efficacy in accurately identifying a wide range of entity types within aviation safety reports. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted the model's robustness, with performance metrics indicating its potential to significantly enhance the automation of aviation safety report analysis. This work addresses a critical gap in English language NER models for aviation safety and establishes a new benchmark for domain-specific NER systems, promising substantial improvements in the analysis and understanding of aviation safety reports.
Keywords
Natural Language Processing; Text Mining; Aviation Safety; NTSB; ASRS; Named Entity Recognition
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
