Exploration of Natural Language Processing (NLP) Applications in Aviation
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Abstract
As a result of the tremendous boost in computational power, the current prevalence of large bodies of data, and the growing power of data-driven algorithms, natural language processing (NLP) has recently experienced rapid progressions in multitudinous domains, one of which is aviation. In this study, we explore the current standing of NLP in aviation from the perspective of both research and industry. We identify safety reports analyses, aviation maintenance, and air traffic control as the three main focus areas of NLP research in aviation. We also list currently available NLP software and how they have been used in the aviation industry. Finally, we shed a spotlight on some of the existing challenges posed by the aviation domain on standard NLP techniques, discuss the current corresponding research efforts, and put forward our recommended research direction.
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References
Abdullah, D., & Takahashi, H. (2016). Innovative high-quality aircraft maintenance by wisdom of semantic database using historical data of operation staffs. 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), 205–210. https://doi.org/10.1109/ISMS.2016.36
Abdullah, D., Takahashi, H., & Lakhani, U. (2017). Improving the understanding between control tower operator and pilot using semantic techniques—A new approach. 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS), 207–211. https://doi.org/10.1109/ISADS.2017.8
Akhbardeh, F., Alm, C. O., Zampieri, M., & Desell, T. (2021). Handling extreme class imbalance in technical logbook datasets. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 4034–4045. https://doi.org/10.18653/v1/2021.acl-long.312
Akhbardeh, F., Desell, T., & Zampieri, M. (2020). Maintnet: A collaborative open-source library for predictive maintenance language resources. Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, 7–11. https://doi.org/10.18653/v1/2020.coling-demos.2
AlgodomMedia. (2020). BytesView, Airline & Airport Operations [Computer Software]. https://www.bytesview.com/industry/airlines
Amherst, MA. (2015, December 15). Lexalytics®’ Text Analytics Software Airlines Industry Pack [Press release]. https://www.lexalytics.com/news/press-releases/text-analytics-helps-airlines-improve-customer-satisfaction
Appareo. (2020). Stratus Insight [Computer Software]. https://stratusinsight.app/
Arnold, A., Dupont, G., Furger, F., Kobus, C., & Lancelot, F. (2020). A question-answering system for aircraft pilots’ documentation. CoRR. https://doi.org/10.48550/arXiv.2011.13284
Badrinath, S., & Balakrishnan, H. (2022). Automatic speech recognition for air traffic control communications. Transportation Research Record: Journal of the Transportation Research Board, 2676(1), 798–810. https://doi.org/10.1177/03611981211036359
Bhatia, A., & Pinto, A. (2021). Automated construction of knowledge-bases for safety critical applications: Challenges and opportunities. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering.
Brundage, M. P., Sexton, T., Hodkiewicz, M., Dima, A., & Lukens, S. (2021). Technical language processing: Unlocking maintenance knowledge. Manufacturing Letters, 27, 42–46. https://doi.org/10.1016/j.mfglet.2020.11.001
Buselli, I., Oneto, L., Dambra, C., Verdonk Gallego, C., GarcÃa MartÃnez, M., Smoker, A., Ike, N., Pejovic, T., & Ruiz Martino, P. (2022). Natural language processing for aviation safety: Extracting knowledge from publicly-available loss of separation reports. Open Research Europe, 1, 110. https://doi.org/10.12688/openreseurope.14040.2
Carchiolo, V., Longheu, A., di Martino, V., & Consoli, N. (2019). Power plants failure reports analysis for predictive maintenance. Proceedings of the 15th International Conference on Web Information Systems and Technologies, 404–410. https://doi.org/10.5220/0008388204040410
Church, J. Q., Walker, L. K., & Bednar, A. E. (2021). Iterative learning algorithm for records analysis (ILARA) user manual [Report]. Information Technology Laboratory (U.S.). https://doi.org/10.21079/11681/41845
CleanSky2. (2020). VOICI [Computer Software]. https://www.sintef.no/projectweb/voici/
Cortical.io. (2011). https://www.cortical.io/free-tools
Dangut, M. D., Skaf, Z., & Jennions, I. K. (2021). An integrated machine learning model for aircraft components rare failure prognostics with log-based dataset. ISA Transactions, 113, 127–139. https://doi.org/10.1016/j.isatra.2020.05.001
Dernoncourt, F., Lee, J. Y., & Szolovits, P. (2017). Neural networks for joint sentence classification in medical paper abstracts. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, 694–700. https://doi.org/10.18653/v1/E17-2110
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. CoRR. https://doi.org/10.48550/arXiv.1810.04805
Dima, A., Lukens, S., Hodkiewicz, M., Sexton, T., & Brundage, M. P. (2021). Adapting natural language processing for technical text. Applied AI Letters, 2(3). https://doi.org/10.1002/ail2.33
Dong, T., Yang, Q., Ebadi, N., Luo, X. R., & Rad, P. (2021). Identifying incident causal factors to improve aviation transportation safety: Proposing a deep learning approach. Journal of Advanced Transportation, 2021, 1–15. https://doi.org/10.1155/2021/5540046
EXSYN Aviation Solutions. (2020). Avilytics [Computer Software]. https://www.exsyn.com/products/avilytics
IBM. (2010). IBM Watson [Computer Software]. https://www.ibm.com/watson
IBM. (2018). IBM Watson Speech to Text [Computer Software]. https://www.ibm.com/cloud/watson-speech-to-text
iSpeech. (2007). https://www.ispeech.org/
Kalyanathaya, K. P., Akila, D., & Rajesh, P. (2019). Advances in natural language processing – A survey of current research trends, development tools and industry applications. International Journal of Recent Technology and Engineering (IJRTE), 7(5C), 199–202.
Kierszbaum, S., & Lapasset, L. (2020). Applying distilled BERT for question answering on ASRS reports. 2020 New Trends in Civil Aviation (NTCA), 33–38. https://doi.org/10.23919/NTCA50409.2020.9291241
Klein, T., Lapasset, L., & Kierszbaum, S. (2021, April). Transformer-based model on aviation incident reports. CORIA 2021. https://hal.archives-ouvertes.fr/hal-03200916
Kuhn, K. D. (2018). Using structural topic modeling to identify latent topics and trends in aviation incident reports. Transportation Research Part C: Emerging Technologies, 87, 105–122. https://doi.org/10.1016/j.trc.2017.12.018
Le, H., Vial, L., Frej, J., Segonne, V., Coavoux, M., Lecouteux, B., Allauzen, A., Crabbé, B., Besacier, L., & Schwab, D. (2020). Flaubert: Unsupervised language model pre-training for French. Proceedings of the 12th Language Resources and Evaluation Conference, 2479–2490. https://aclanthology.org/2020.lrec-1.302
LexXTechnologies. (2018). LexX Air [Computer Software]. https://lexxtechnologies.com/aviation/
Lin, J., Su, Q., Yang, P., Ma, S., & Sun, X. (2018). Semantic-unit-based dilated convolution for multi-label text classification. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4554–4564. https://doi.org/10.18653/v1/D18-1485
Lin, Y. (2021). Spoken instruction understanding in air traffic control: Challenge, technique, and application. Aerospace, 8(3), 65. https://doi.org/10.3390/aerospace8030065
LingaKit. (2018). https://linguakit.com/en/full-analysis
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pre-training approach. CoRR. https://doi.org/10.48550/arXiv.1907.11692
Madeira, T., MelÃcio, R., Valério, D., & Santos, L. (2021). Machine learning and natural language processing for prediction of human factors in aviation incident reports. Aerospace, 8(2), 47. https://doi.org/10.3390/aerospace8020047
Marev, K., & Georgiev, K. (2019). Automated aviation occurrences categorization. 2019 International Conference on Military Technologies (ICMT), 1–5. https://doi.org/10.1109/MILTECHS.2019.8870055
Marques, H. C., Giacotto, A., Scussiatto, C. E., & Abrahão, F. T. M. (2021). Semantic voice search in IETP: Filling the gap for maintenance 4.0. Journal of Quality in Maintenance Engineering, 27(3), 500–516. https://doi.org/10.1108/JQME-05-2020-0038
Martin, L., Muller, B., Ortiz Suárez, P. J., Dupont, Y., Romary, L., de la Clergerie, É., Seddah, D., & Sagot, B. (2020). Camembert: A tasty French language model. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7203–7219. https://doi.org/10.18653/v1/2020.acl-main.645
Mosaic ATM. (2004). https://mosaicatm.com/
Nandyala, A. V., Lukens, S., Rathod, S., & Agarwal, P. (2021). Evaluating word representations in a technical language processing pipeline. PHM Society European Conference, 6, 17–17.
Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28, III-1310-III–1318.
Paul, S. (2021). Understanding aviation English: Challenges and opportunities in NLP applications for Indian languages. Air Traffic Management and Control [Working Title], 87. https://doi.org/10.5772/intechopen.99612
Perboli, G., Gajetti, M., Fedorov, S., & Giudice, S. L. (2021). Natural Language Processing for the identification of Human factors in aviation accidents causes: An application to the SHEL methodology. Expert Systems with Applications, 186, 115694. https://doi.org/10.1016/j.eswa.2021.115694
Pimm, C., Raynal, C., Tulechki, N., Hermann, E., Caudy, G., & Tanguy, L. (2012). Natural Language Processing (NLP) tools for the analysis of incident and accident reports. International Conference on Human-Computer Interaction in Aerospace (HCI-Aero). https://halshs.archives-ouvertes.fr/halshs-00953658
Roadmap, A. I. (2020). AI Roadmap: A human-centric approach to AI in aviation. European Aviation Safety Agency, 1. https://semiengineering.com/ai-roadmap-a-human-centric-approach-to-ai-in-aviation/
Robinson, S. D. (2019). Temporal topic modeling applied to aviation safety reports: A subject matter expert review. Safety Science, 116, 275–286. https://doi.org/10.1016/j.ssci.2019.03.014
Rose, R. L., Puranik, T. G., & Mavris, D. N. (2020). Natural language processing based method for clustering and analysis of aviation safety narratives. Aerospace, 7(10), 143. https://doi.org/10.3390/aerospace7100143
Selcuk, S. (2017). Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(9), 1670–1679. https://doi.org/10.1177/0954405415601640
Sun, Z., & Tang, P. (2021). Automatic communication error detection using speech recognition and linguistic analysis for proactive control of loss of separation. Transportation Research Record: Journal of the Transportation Research Board, 2675(5), 1–12. https://doi.org/10.1177/0361198120983004
SparkCognition. (2018). DeepNLP [Computer Software]. https://www.sparkcognition.com/products/deepnlp/
Suzgun, M., Belinkov, Y., & Shieber, S. M. (2019). On evaluating the generalization of LSTM models in formal languages. Proceedings of the Society for Computation in Linguistics (SCiL) 2019, 277–286. https://doi.org/10.7275/s02b-4d91
Tanguy, L., Tulechki, N., Urieli, A., Hermann, E., & Raynal, C. (2016). Natural language processing for aviation safety reports: From classification to interactive analysis. Computers in Industry, 78, 80–95. https://doi.org/10.1016/j.compind.2015.09.005
Tulechki, N. (2015). Natural language processing of incident and accident reports: Application to risk management in civil aviation [PhD thesis, Université Toulouse le Mirail - Toulouse II]. https://tel.archives-ouvertes.fr/tel-01230079
Usuga-Cadavid, J. P., Lamouri, S., Grabot, B., & Fortin, A. (2021). Using deep learning to value free-form text data for predictive maintenance. International Journal of Production Research, 1–28. https://doi.org/10.1080/00207543.2021.1951868
Wang, X., Wang, G., & Xu, Q.-C. (2019). A new structural template design of control instruction for semantic analysis. CICTP 2019, 2935–2945. https://doi.org/10.1061/9780784482292.254
Zhang, X., Srinivasan, P., & Mahadevan, S. (2021). Sequential deep learning from NTSB reports for aviation safety prognosis. Safety Science, 142, 105390. https://doi.org/10.1016/j.ssci.2021.105390
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schölkopf, B. (2003). Learning with local and global consistency. Proceedings of the 16th International Conference on Neural Information Processing Systems, 321–328.