Research Article
A Hybrid Named Entity Recognition System for Aviation Text
@ARTICLE{10.4108/eetsis.4185, author={Bharathi A and Robin Ramdin and Preeja Babu and Vijay Krishna Menon and Chandrasekhar Jayaramakrishnan and Sudarasan Lakshmikumar}, title={A Hybrid Named Entity Recognition System for Aviation Text}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={11}, number={1}, publisher={EAI}, journal_a={SIS}, year={2023}, month={10}, keywords={Named Entity Recognition, Machine Learning, Aviation Herald, Spacy NER, GPE, Rule Augmentation}, doi={10.4108/eetsis.4185} }- Bharathi A
Robin Ramdin
Preeja Babu
Vijay Krishna Menon
Chandrasekhar Jayaramakrishnan
Sudarasan Lakshmikumar
Year: 2023
A Hybrid Named Entity Recognition System for Aviation Text
SIS
EAI
DOI: 10.4108/eetsis.4185
Abstract
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.
Copyright © 2023 Bharathi A et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.