A Review of the Development and Application of Natural Language Processing

  • Wei-Wen GuoEmail author
  • Li-Li Huang
  • Jeng-Shyang Pan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1107)


With the development of convolutional neural networks and deep learning and a series of very significant breakthroughs in computer speech, many new models and methods have been provided for the field of Natural language processing. Natural language processing is a very important branch of artificial intelligence, and its application requirements and relevant fields are also becoming wider and wider. This paper first summarizes the related concepts of Natural language processing; then introduces in detail the development process of Natural language processing; then elaborates on the research progress of the application field of Natural language processing, including lexical analysis, syntactic analysis, machine translation and other fields; finally, the semantic understanding, the problem of low resources and the development direction of other fields are summarized and forecasted.


NLP Artificial intelligence Lexical analysis Machine translation 



This work was funded by the Education and Research Projects of Fujian University of Technology, numbered JXKA18015, GB-M-17-11, and GY-Z15101; and Foundation for Scientific Research of Fujian Education Committee (JAT170371).


  1. 1.
    Zong, C.: Statistical NLP, pp. 2–3. Tsinghua University Press, Beijing (2013)Google Scholar
  2. 2.
    Kaplan, J.: Artificial Intelligence Era, pp. 11–45. Zhejiang. Zhejiang People’s Publishing House, Hangzhou (2016)Google Scholar
  3. 3.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. Comput. Sci. (2013)Google Scholar
  4. 4.
    Schuster, M., Palimal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)CrossRefGoogle Scholar
  5. 5.
    Cho, K., Merrienboer, B., Gulceher, C., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014). arXiv Preprint arXiv:
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Xing, L.: Design of automatic translation generation system in English-Chinese machine translation. Mod. Electron. Technol. (2018)Google Scholar
  8. 8.
    Giguet, E., Vergne, J.: Syntactic analysis of unrestricted French. In: Proceedings for the International Conference on Recent Advances in Natural Languages Processing (RANLP97), pp. 276–281 (1997)Google Scholar
  9. 9.
    Katz, B., Marton, G., Borchardt, G., et al.: The START Natural Language Question Answering System [ EB/OL] (2006)Google Scholar
  10. 10.
    Zheng, Z.: AnswerBus Question Answering System [EB/OL] (2006)Google Scholar
  11. 11.
    Ittycheriah, A., Roukos, A.: IBM’s statistical question answering system. In: Proceedings of the TREC-11 Conference, pp. 394–401. NIST Special Publication, Gaithersburg (2002)Google Scholar
  12. 12.
    Cao, C.-G.: NKI-21 century technology hotspot. Comput. World 5(2), 1–3 (1998)Google Scholar
  13. 13.
    Dong, L., Wei, F., Zhou, M., et al.: Question answering over freebase with multi-column convolutional neural networks. In: Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pp. 260–269 (2015)Google Scholar
  14. 14.
    Qin, B., Liu, T., Li, S.: Multi-document automatic summarization review. Chin. J. Inf. 19(6), 13–20 (2005)Google Scholar
  15. 15.
    Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264–285 (1969)CrossRefGoogle Scholar
  17. 17.
    Conroy, J., O’leary, D.P.: Text summarization via hidden markov models and pivoted qr matrix decomposition. Technical report, In SIGIR (2001)Google Scholar
  18. 18.
    Osborne, M.: Using maximum entropy for sentence extraction. In: Proceedings of the ACL-Q2 Workshop on Automatic Summarization-Volume4, AS 2002, pp. 1–8 (2002). palesGoogle Scholar
  19. 19.
    Liu, Y., Zhong, S.H., Li, W.: Query-oriented multi-document summarization via unsupervised deep learning. In: AAAI’12 Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 1699–1705. AAAI Press, Palo Alto (2012)Google Scholar
  20. 20.
    Abadi, M., Agarwal, A., Barham, P., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems (2016). arXiv preprint arXiv:1603.04467
  21. 21.
    Kalchbrenner, N., Blunson, P.: Recurrent continuous translation models. In: Proceedings of the 2013 Conference on Empirical Methods in NLP, pp. 1700–1709 (2013)Google Scholar
  22. 22.
    Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  23. 23.
    He, W., He, Z., Wu, H., et al.: Improved neural machine translation with SMT features. In: AAAI, pp. 151–157 (2016)Google Scholar
  24. 24.
    Tang, X.-B., Yan, C.-X.: A sentimental classification model based on SPIPR() principle and support vector machine. Inf. Stud.: Theory Appl. 36(1), 98–103 (2013)MathSciNetGoogle Scholar
  25. 25.
    Manek, A.S., Shenoy, P.D., Mohan, M.C., et al.: Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web-internet Web Inf. Syst. 20(2), 135–154 (2017)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Fujian University of TechnologyFuzhouChina
  2. 2.Fujian Provincial Key Laboratory of Big Data Mining and ApplicationsFujian University of TechnologyFuzhouChina

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