A Hybrid Named Entity Recognition System for Aviation Text

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that aims to identify and categorize named entities in text. While NER has been well-studied in various domains, it remains a challenging task in new domains where annotated data is limited. In this paper, we propose an NER system for the aviation domain that addresses this challenge. Our system combines rule-based and supervised methods to develop a model with little to no manual annotation work. We evaluate our system on a benchmark dataset and it outperforms baseline scores and achieves competitive results. To the best of our knowledge, this is the first study to develop an NER system that specifically targets aviation entities. Our findings highlight the potential of our proposed system for NER in aviation and pave the way for future research in this area.