Applications of natural language processing in aviation safety: A review and qualitative analysis

TL;DR

This review examines how natural language processing techniques are applied in aviation safety, highlighting current research, challenges, and future directions to enhance safety measures through machine learning and AI.

Contribution

It provides a comprehensive qualitative analysis of NLP applications in aviation safety, identifying research gaps and proposing practical solutions for implementation challenges.

Findings

NLP helps identify critical safety issues in aviation.

Case studies show successful NLP applications improving safety.

Challenges include data annotation and model interpretability.

Abstract

This study explores using Natural Language Processing in aviation safety, focusing on machine learning algorithms to enhance safety measures. There are currently May 2024, 34 Scopus results from the keyword search natural language processing and aviation safety. Analyzing these studies allows us to uncover trends in the methodologies, findings and implications of NLP in aviation. Both qualitative and quantitative tools have been used to investigate the current state of literature on NLP for aviation safety. The qualitative analysis summarises the research motivations, objectives, and outcomes, showing how NLP can be utilized to help identify critical safety issues and improve aviation safety. This study also identifies research gaps and suggests areas for future exploration, providing practical recommendations for the aviation industry. We discuss challenges in implementing NLP in aviation safety, such as the need for large, annotated datasets, and the difficulty in interpreting complex models. We propose solutions like active learning for data annotation and explainable AI for model interpretation. Case studies demonstrate the successful application of NLP in improving aviation safety, highlighting its potential to make aviation safer and more efficient.