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An ATC instruction processing-based trajectory prediction algorithm designing

  • S.I: Human-in-the-loop Machine Learning
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Abstract

The radiotelephony communication is a voice communication mode between air traffic service unit and aircraft currently. The control instruction is a kind of unstructured data, so that the automatic systems cannot use understand its semantic. If control instruction is regarded as a sort of special “natural language,” methods such as syntax analysis and sematic analysis can be adopted to generate the structured instruction. The correct recognition of the language must be important for the control instruction. However, the control instruction in Chinese is different from the general use of Chinese language in form, resulting in prepositions becoming important for semantic analysis. This paper proposes a deep neural network-based Chinese language control construction algorithm for the trajectory prediction. In particular, analysis of sematic characteristics of control instruction is realized by using cognitive linguistics theory and construction grammar theory. The control instruction is then designed by the semantic ontology. Based on the deep neural networks by considering the word sequence of instruction as the inputs. The test results have demonstrated the effectiveness of the proposed algorithm with a developed entity extracting model. (The results are quantified using the BiLSTM-LAN-CRF in detail.)

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References

  1. Helmke H, Slotty M, Poiger M, et al (2018) Ontology for transcription of ATC speech commands of SESAR 2020 solution PJ.16–04. In: 37th digital avionics systems conference (DASC)

  2. Nguyen VN, Holone H (2016) N-best list re-ranking using syntactic score: a solution for improving speech recognition accuracy in Air Traffic Control. In: 16th int. conf. on control, automation and systems (ICCAS 2016), pp 1309–1314

  3. Nguyen VN, Holone H (2016) N-best list re-ranking using syntactic relatedness and syntactic score: an approach for improving speech recognition accuracy in Air Traffic Control. In: 16th int. conf. on control, automation and systems (ICCAS 2016), pp 1315–1319

  4. Wang X, Wang G, Cheng XQ (2019) A new structural template design of control instruction for semantic analysis. In: Proceedings of CICTP, pp 2935–2945

  5. Goldberg AE (1985) Construction: a construction grammar approach to argument structure. The University of Chicago Press, Chicago

    Google Scholar 

  6. Wang SQ (2016) Research of prepositions in contemporary Chinese (in Chinese). Nanjing University Press, Nanjing

    Google Scholar 

  7. Fillmore CJ (1968) The case for case. In: Bach E, Harms RT (eds) Universals in linguistic theory. Holt, Rinehart, New York

    Google Scholar 

  8. Yuan YL (2010) Research on Chinese valency theory (in Chinese). The Commercial Press, Beijing

    Google Scholar 

  9. Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, pp 282–289

  10. Hammerton J (2003) Named entity recognition with long short-term memory. In: HLT-NAACL 2003, vol 4, pp 172–175

  11. Kiperwasser E, Goldberg Y (2016) Simple and accurate dependency parsing using bidirectional LSTM feature representations. TACL 4:313–327

    Article  Google Scholar 

  12. Dozat T, Manning C (2017) Deep biaffine attention for neural dependency parsing. In: ICLR

  13. Zhang Y, Yang J (2018) Chinese NER using lattice LSTM. In: ACL, pp 1554–1564

  14. Collobert R, Weston J, Bottou L, Karlen M et al (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    MATH  Google Scholar 

  15. Santos C, Guimaraes V (2015) Boosting named entity recognition with neural character embeddings. In: ACL, pp 25–33

  16. Chiu J, Nichols E (2016) Named entity recognition with bidirectional LSTM-CNNS. In: ACL, pp 357–370

  17. Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991

  18. Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Natural architectures for named entity recognition. In: ACL, pp 260–270

  19. Ma X, Hovy E (2016) End-to-end sequence labeling via vi-directional LASTM-CNNS-CRF. In: ACL, pp 1064–1074

  20. Vaswani A, Shazeer N, Parmer N, Uszkoreit J, et al (2017) Attention is all you need. In: Advances in natural information processing system, pp 6000–6010

  21. Cao P, Chen Y, Liu K, Liu S (2018) Adversarial transfer learning for Chinese named entity recognition with self-attention mechanism. In: Conf. on empirical methods in natural language processing, pp 182–192

  22. Cui L, Zhang Y (2019) Hierarchically-refined label attention network for sequence labeling. In: ACL, pp 4115–4128

  23. Chen Y, Xu L, Liu K, Zeng D, Zhao J (2015) Event extraction via dynamic multi-pooling convolutional neural networks. ACL 1:167–176

    Google Scholar 

  24. Nguyen T, Cho K, Grishman R (2016) Joint event extraction via recurrent neural networks. In: Conf. on the North American Chapter of ACL: human language technologies, pp 300–309

  25. He L, Lee K, Lewis M, Zettlemoyer L (2017) Deep semantic role labeling: what works and what’s next. In: Proceedings of the 55th annual meeting of the association for computational linguistics, pp 473–483

  26. Tan Z, Wang M, Xie J, Chen Y, Shi X (2017) Deep semantic role labeling with self-attention. arXiv preprint arXiv:1712.01586

  27. Strubell E, Verga P, Andor D, Weiss D, McCallum A (2018) Linguistically-informed self-attention for semantic role labeling. In: ACL, pp 5027–5038

  28. Guha RV, McCool R, Miller E (2003) Semantic search. In: Proceeding of the 12th international World Wide Web conference, pp 700–709

  29. Wang WL, Zhao Q (2016) Radiotelephony communications for air traffic controllers (in Chinese). Tsinghua University Press, Beijing

    Google Scholar 

  30. Newman J (2004) Motivating the uses of basic verbs: linguistic and extralinguistic considerations. In: Radden G, Panther K-U (eds) Studies in linguistic motivation. Mouton de Gruyter, Berlin, pp 193–218

    Google Scholar 

  31. Hiraga MK (1994) Diagrams and metaphors: iconic aspects in language. J Pragmat 22:5–21

    Article  Google Scholar 

  32. Lakoff G (1987) Woman, fire and dangerous things: what categories reveal about the world. The University of Chicago Press, Chicago

    Book  Google Scholar 

  33. Lin GX, Wang LL, Sun DJ (1994) Dictionary of verbs in contemporary Chinese (in Chinese). Beijing Language Institute Press, Beijing

    Google Scholar 

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Acknowledgements

This research is supported by the National Key Research and Development Project (No. 2020YFB2104204) and the China Postdoctoral Science Foundation (No. 2020M681750)

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Authors and Affiliations

  1. State Key Laboratory of Air Traffic Management System and Technology, Nanjing, 210007, China

    Xuan Wang, Yi Mao, Qucheng Xu & Weiyu Jiang

  2. JiangSu ATM Sub-BUREAU, Nanjing, 210000, China

    Xiaoyong Wu

  3. Department of Civil and Environmental Engineering, Imperial College London, London, SW7 2AZ, UK

    Suwan Yin

Authors
  1. Xuan Wang
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  2. Yi Mao
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  3. Xiaoyong Wu
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  4. Qucheng Xu
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Correspondence to Yi Mao.

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Wang, X., Mao, Y., Wu, X. et al. An ATC instruction processing-based trajectory prediction algorithm designing. Neural Comput & Applic 35, 23477–23490 (2023). https://doi.org/10.1007/s00521-021-05713-4

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