用NLP分析航空安全数据，发现隐患并提升飞行安全
Applications of natural language processing in aviation safety: A review and qualitative analysis
- 结合定性与定量方法分析34篇文献，梳理NLP在航空安全中的应用趋势
- 揭示需大量标注数据和可解释模型的挑战，提出主动学习与可解释AI方案
- 适合航空安全研究者与技术决策者参考，推动智能安全系统落地
本研究探讨自然语言处理（NLP）在航空安全中的应用，基于2024年5月前的Scopus数据库中检索到的34篇相关文献进行综述。通过定性与定量分析，总结了现有研究的动机、目标与成果，揭示了NLP在识别关键安全问题、提升航空安全方面的潜力。研究指出当前主要挑战包括缺乏大规模标注数据及复杂模型的可解释性难题，并提出主动学习用于数据标注、可解释AI用于模型解读等解决方案。多个案例表明NLP在实际航空安全管理中已取得成功应用，展现出提升航空安全性和效率的巨大前景。研究还识别出若干未充分探索的方向，为未来研究提供实践建议。
原文摘要 · Abstract (English)
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.
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