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Ten-Years Research Progress of Natural Language Understanding Based on Perceptual Formalization

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Intelligence Science II (ICIS 2018)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 539))

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Abstract

This paper introduces the research progress of machinery truly understanding of natural language from three aspects. First, this paper explains why to carry out data or feature description by perceptual structure. Secondly, this paper summarizes the main understanding algorithms since the theory of machinery truly understanding has been proposed, and emphasizes the recent research progress. Finally, in view of current research status, this paper gives some research directions of natural language understanding in the future.

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References

  1. King, M.: When is the next Alpac report due? In: International Conference on Computational Linguistics and Meeting on Association for Computational Linguistics, pp. 352–353. Association for Computational Linguistics (1984)

    Google Scholar 

  2. ALPAC: Language and Machines: Computers in Translation and Linguistics. Nation Academy of Sciences, Washington D.C. (1966)

    Google Scholar 

  3. Fillmore, C.J.: Some problems for case grammar. In: 22nd Annual Round Table. Linguistics: Developments of the Sixties-Viewpoints of the Seventies, pp. 35–56. Georgetown University Press, Washington, D.C. (1971)

    Google Scholar 

  4. Zou, C.L., Cui, J.Y.: A study of category type logic based on case grammar. J. Anhui Univ. Philos. Soc. Sci. 38(4), 15–22 (2014)

    Google Scholar 

  5. Schank, R.C.: Conceptual information processing. Inf. Process. Manag. 22(86), 1–3 (1975)

    MATH  Google Scholar 

  6. Miller, G.A., Beckwith, R., Fellbaum, C., et al.: Introduction to WordNet: an on_Line lexical database. In: CSL Report, Princeton University, Princeton (1993)

    Google Scholar 

  7. Pan, Y.N., Teng, H.M.: Word sense disambiguation of tags in XML documents based on WordNet. Inf. Sci. 3, 24 (2014)

    Google Scholar 

  8. Wei, T., Zhou, Q., Chang, H., et al.: A semantic approach for text clustering using WordNet and lexical chains. Expert Syst. Appl. 42, 2264–2275 (2015)

    Article  Google Scholar 

  9. Sharan, A., Joshi, M.L.: An algorithm for finding document concepts using semantic similarities from WordNet ontology. Int. J. Comput. Vis. Robot. 1(2), 147–157 (2010)

    Article  Google Scholar 

  10. Sotolongo, R., Yan, D., Hirota, K.: Algorithm for code clone refinement based on semantic analysis of multiple detection reports using WordNet. In: Proceedings of the 4th International Symposium on Computational Intelligence and Industrial Applications, pp. 191–198. Publishing House of Electronics Industry (2010)

    Google Scholar 

  11. Janzen, S., Maass, W.: Ontology-based natural language processing for in-store shopping situations. In: IEEE International Conference on ICSC 2009, pp. 361–366. IEEE (2009)

    Google Scholar 

  12. Minu, R.I., Thyagharajan, K.K.: Semantic rule based image visual feature ontology creation. Int. J. Autom. Comput. 11(5), 489–499 (2014)

    Article  Google Scholar 

  13. Lu, L.Q.: Knowledge Engineering and Science at the Turn of the Century, vol. 9, pp. 447–497. Tsinghua publishing house, Beijing (2001)

    Google Scholar 

  14. Winograd, T.: Language as a Cognitive Process. Addison Wesley, Boston (1983)

    MATH  Google Scholar 

  15. Zhong, Y.X.: Full information methodology for natural language understanding. J. Beijing Univ. Posts Telecommun. 4, 1–12 (2004)

    MathSciNet  Google Scholar 

  16. Liu, S.: Research and Answer System Based on the Full Information. Beijing University of Posts and Telecommunications, Haidian (2014)

    Google Scholar 

  17. Huang, Z.Y.: HNC theory and understanding of natural language statement. Chin. Basic Sci. z1, 85–90 (1999)

    Google Scholar 

  18. Liu, Z.Y.: Construction of HNC word knowledge base serving Chinese English Machine Translation. Lang. Appl. 1, 117–126 (2015)

    Google Scholar 

  19. Dong, Z.D.: HowNet (2017). http://www.keenage.com

  20. Ma, X.W.: Natural language understanding. Comput. Eng. Appl. 4, 20–23 (1987)

    Google Scholar 

  21. Jia, Y.X., Yu, S.W., Zhu, X.F.: Research progress in automatic metaphor processing. Chin. J. Inf. 23(6), 46–55 (2009)

    Google Scholar 

  22. Yu, S.W.: Natural language understanding and grammar research. In: Ma, Q.Z. (ed.) Introduction to Grammar Research, pp. 240–251. Commercial Press, Beijing (1999)

    Google Scholar 

  23. Wallace, R.S.: The anatomy of ALICE. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test, pp. 181–210. Springer, Dordrecht (2009). https://doi.org/10.1007/978-1-4020-6710-5_13

    Chapter  Google Scholar 

  24. Lu, R.Z.: Concept, semantic computation and connotation logic. In: Some Important Issues on Chinese Information Processing. Beijing: Science Press, pp. 90–99 (2003)

    Google Scholar 

  25. Li, Z.J., Lu, R.Z.: Research on the generation method of user requirement concept map. Comput. Appl. Softw. 29(1), 23–26 (2012)

    Google Scholar 

  26. Chandrasekar, R.: Elementary? Question answering, IBM’s Watson, and the Jeopardy! challenge. Resonance 19(3), 222–241 (2014)

    Article  Google Scholar 

  27. Shi, Z.Z.: Cognitive Science, pp. 1–591. University of Science and Technology of China press, Hefei (2008)

    Google Scholar 

  28. Huang, P.H.: Formalization of natural language understanding. Comput. Eng. Sci. 29(6), 113–116 (2007)

    Google Scholar 

  29. Huang, P.H.: NLU-A logic theory on machine perceiving language of humankind. J. Sichuan Ordnance 30(1), 138–142 (2009)

    MathSciNet  Google Scholar 

  30. Huang, P.H.: A method of machinery inference learning based on text explaining. J. Chongqing Commun. Inst. 29(6), 89–91 (2009)

    Google Scholar 

  31. Huang, P.H.: What–why effect on natural language understanding. Mod. Comput. Res. Dev. 10, 9–16 (2016)

    Google Scholar 

  32. Hume: Human Understanding Study, pp. 19–66. Commercial Press, Beijing (1957)

    Google Scholar 

  33. Huang, P.H.: Study on cognitive system based on natural language understanding-algorithm foundation and construction of man-machine dialogue system. In: The 8th Symposium of Computer Science and Technology for ICT Graduate Students of CAS, pp. 23–24 (2004)

    Google Scholar 

  34. Qien, X.S.: About Thinking Science. Shanghai People’s Publishing House, Shanghai (1986)

    Google Scholar 

  35. Jayant, K., Thomas, K.: Jointly learning to parse and perceive: connecting natural language to the physical world. TACL 1, 193–206 (2013)

    Google Scholar 

  36. Emanuele, B., Danilo, C., Andrea, V., Roberto, B., Daniele, N.: A discriminative approach to grounded spoken language understanding in interactive robotics. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, pp. 2748–2753. Morgan Kaufmann, New York (2016)

    Google Scholar 

  37. Huang, P.: Exploration on causal law of understanding and fusion linking of natural language. In: Shi, Z., Goertzel, B., Feng, J. (eds.) ICIS 2017. IAICT, vol. 510, pp. 344–350. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68121-4_37

    Chapter  Google Scholar 

  38. Morten, T.: The Imitation Game (2017). https://en.wikipedia.org/wiki/The_Imitation_Game

  39. Feng, Z.W.: Machine translation’s current situation and problems. In: Some Important Issues in Chinese Information Processing, pp. 353–377. Science Press (2003)

    Google Scholar 

  40. Huang, P.H., Zheng, G.L.: Study on definition of intelligence by combining with primitive semantics. Mind Comput. 1(3), 193–202 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Putian Branch of Agriculture Bank of China, Putian, China

    Peihong Huang

  2. School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China

    Guo-Lei Zheng

  3. School of Computer, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China

    Shilong Ma

Authors
  1. Peihong Huang
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  2. Guo-Lei Zheng
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  3. Shilong Ma
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Corresponding author

Correspondence to Peihong Huang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Amsterdam, Amsterdam, The Netherlands

    Cyriel Pennartz

  3. Peking University, Beijing, China

    Tiejun Huang

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© 2018 IFIP International Federation for Information Processing

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Huang, P., Zheng, GL., Ma, S. (2018). Ten-Years Research Progress of Natural Language Understanding Based on Perceptual Formalization. In: Shi, Z., Pennartz, C., Huang, T. (eds) Intelligence Science II. ICIS 2018. IFIP Advances in Information and Communication Technology, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-01313-4_20

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  • DOI: https://doi.org/10.1007/978-3-030-01313-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01312-7

  • Online ISBN: 978-3-030-01313-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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