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Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model

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Publicado en:Aerospace vol. 12, no. 5 (2025), p. 376
Autor principal: Chen, Sheng
Otros Autores: Pan Weijun, Wang, Yidi, Chen Shenhao, Wang, Xuan
Publicado:
MDPI AG
Materias:
Accuracy
Traffic
Aircraft accidents & safety
Flight safety
Deep learning
Air traffic control
Air traffic management
Conditional random fields
Data collection
Automation
Information retrieval
Optimization
Aviation
Document management
Real time
Keywords
Pilots
Efficiency
Semantics
Acceso en línea:Citation/Abstract
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Resumen:In recent years, with the increasing complexity of air traffic management and the rapid development of automation technology, efficiently and accurately extracting key information from large volumes of air traffic control (ATC) instructions has become essential for ensuring flight safety and improving the efficiency of air traffic control. However, this task is challenging due to the specialized terminology involved and the high real-time requirements for data collection and processing. While existing keyword extraction methods have made some progress, most of them still perform unsatisfactorily on ATC instruction data due to issues such as data irregularities and the lack of domain-specific knowledge. To address these challenges, this paper proposes a Roberta-Attention-BiLSTM-CRF model for keyword extraction from ATC instructions. The RABC model introduces an attention mechanism specifically designed to extract keywords from multi-segment ATC instruction texts. Moreover, the BiLSTM component enhances the model’s ability to capture detailed semantic information within individual sentences during the keyword extraction process. Finally, by integrating a Conditional Random Field (CRF), the model can predict and output multiple keywords in the correct sequence. Experimental results on an ATC instruction dataset demonstrate that the RABC model achieves an accuracy of 89.5% in keyword extraction and a sequence match accuracy of 91.3%, outperforming other models across multiple evaluation metrics. These results validate the effectiveness of the proposed model in extracting keywords from ATC instruction data and demonstrate its potential for advancing automation in air traffic control.
ISSN:2226-4310
DOI:10.3390/aerospace12050376
Fuente:Advanced Technologies & Aerospace Database