Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model
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 improvi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/5/376 |
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| author | Sheng Chen Weijun Pan Yidi Wang Shenhao Chen Xuan Wang |
| author_facet | Sheng Chen Weijun Pan Yidi Wang Shenhao Chen Xuan Wang |
| author_sort | Sheng Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1c8814aa913e41ba9ba3adf79a46d31a |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-1c8814aa913e41ba9ba3adf79a46d31a2025-08-20T02:33:39ZengMDPI AGAerospace2226-43102025-04-0112537610.3390/aerospace12050376Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF ModelSheng Chen0Weijun Pan1Yidi Wang2Shenhao Chen3Xuan Wang4College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaCollege of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, ChinaIn 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.https://www.mdpi.com/2226-4310/12/5/376keyword extractiondeep learningRobertaattention mechanismair traffic managementair traffic control |
| spellingShingle | Sheng Chen Weijun Pan Yidi Wang Shenhao Chen Xuan Wang Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model Aerospace keyword extraction deep learning Roberta attention mechanism air traffic management air traffic control |
| title | Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model |
| title_full | Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model |
| title_fullStr | Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model |
| title_full_unstemmed | Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model |
| title_short | Research on the Method of Air Traffic Control Instruction Keyword Extraction Based on the Roberta-Attention-BiLSTM-CRF Model |
| title_sort | research on the method of air traffic control instruction keyword extraction based on the roberta attention bilstm crf model |
| topic | keyword extraction deep learning Roberta attention mechanism air traffic management air traffic control |
| url | https://www.mdpi.com/2226-4310/12/5/376 |
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