State-of-the-Art Natural Language Processing for Aviation: A Review
Utkarsha Singh, Margamitra Bhattacharya, Radhakant Padhi · 2025
The rise of large-scale data and automated processes has enabled the widespread adoption of artificial intelligence (AI) across various industries. The aviation sector, renowned for its critical operations, has recently embraced this technological innovation. The availability of aviation data has paved the way for extensive AI research opportunities. Within aviation, Natural Language Processing (NLP) has emerged as a key area of interest. By harnessing textual information to automate tasks, NLP significantly reduces manual labour and time requirements. This paper presents a systematic review of 66 papers on NLP applications in the aviation sector. The review aims to assist researchers in identifying crucial information for conducting further research and development in aviation utilizing NLP. Prior studies are reviewed and categorized based on the various application areas within the aviation domain where NLP has been employed, as well as the approaches like rule, machine learning (ML), deep learning (DL), and hybrid employed for its implementation. It also discusses the open-source data utilized in previous work for implementing NLP applications in aviation, along with the metrics used for model evaluation. Furthermore, it highlights the approach and model most commonly used, the application area where NLP has been applied most frequently, and some ideas for future directions.