Fuzzy Sets in Natural Language Processing

  • Vilém Novák
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 165)


Natural language is one of the most complicated structures a man has met with. It plays a fundamental role not only in human communication but even in human way of thinking and regarding the world. Therefore, it is extremely important to study it in all its respects. Much has been done in understanding its structure, especially the phonetic and syntactic aspects. Less, however, is understood its semantics. There are many linguistic systems, often based on set theory and logic, attempting to grasp (at least some phenomena) of the natural language. However, none them is fully acceptted and satisfactory in all respects.


Membership Function Natural Language Natural Language Processing Membership Degree Natural Language Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    Bezdek, J. (ed.), Analysis of Fussy Information - Vol. 1: Mathematics and Logic, CRC Press, Boca Raton, Fl. 1987.Google Scholar
  2. [2]
    Bezdek, J. (ed.), Analysis of Fussy Information - Vol. 2: Artificial Intelligence and Decision Systems, CRC Press, Boca Raton, Fl. 1987.Google Scholar
  3. [3]
    Bezdek, J. (ed.), Analysis of Fussy Information - Vol. S: Applications in Engineering and Science, CRC Press, Boca Raton, FL 1987.Google Scholar
  4. [4]
    Dubois, D., Prade, H., Fussy Sets and ystems:Theory and Applications, Academic Press, New York 1980.Google Scholar
  5. [5]
    Esragh, F., Mamdani, E.H.A generalapproachto linguistic approximation Int.J. Man-Mach. Stud., 11(1979), 501–519.CrossRefGoogle Scholar
  6. [6]
    Gaines, B.R., Booee, J.H. (eds.), Machine Learning and Uncertain Reasoning, Academic Press, London 1990.zbMATHGoogle Scholar
  7. [7]
    Gärdenfors, P. (ed.), Generalised quantifiers, D. Reidel, Dordecht, 1987.Google Scholar
  8. [8]
    Gupta, M.M., Yamakawa, T. (eds.), Fussy Computing: Theory, Hardware and Applications, North—Holland, Amsterdam 1988.Google Scholar
  9. [9]
    Gupta, M.M., Yamakawa, T. (eds.), Fussy Logic in Knowledge-Based Systems, Decision and Control, North-Holland, Amsterdam 1988.Google Scholar
  10. [10]
    Kuz’min, V. B.About semantleal structure of linguistic hedges:anexperimentalhypothesisBUSEFAL 24, 1985,118–125, Université Paul Sabatier, Toulouse.Google Scholar
  11. [11]
    Lakoff, G.Hedges: A studyin meaning criteria andlogic of fuzzy conceptsJ. Philos. Logic 2(1973), 458–508.MathSciNetzbMATHCrossRefGoogle Scholar
  12. [12]
    Mamdani, E.H., Gaines, B.R. (eds.), Fussy Reasoning and its Applications, Academic Press, London 1981.Google Scholar
  13. [13]
    Novtk, V., Fussy Sets and Their Applications, Adam-Hilger, Bristol, 1989.Google Scholar
  14. [14]
    Novtk, V., The Alternative Mathematical Model of Natural Language Semantics. Manuscript. Mining Institute, Ostrava 1989. (To be published by Cambridge University Press)Google Scholar
  15. [15]
    Novtk, V., Pedrycs, W.Fuzzy sets and t-normsinthe light of fuzzy logicInt. J. Man-Mach. Stud., 29(1988), 113–127.CrossRefGoogle Scholar
  16. [16]
    Pavelka, J., Onfuzzy logic I II III Zeit. Math.Logic. Grundl. Math. 25(1979), 45–52, 119–134, 447–464.Google Scholar
  17. [17]
    Sgall, P. (ed.), Contributions to functional syntax, semantics, and language comprehension, Academia, Prague 1984.Google Scholar
  18. [18]
    Sgall, P., Hajicovt, E., Panevovt, J., The meaning of the sentence in its semantic and pragmatic aspects, D. Reidel, Dordecht 1986.Google Scholar
  19. [19]
    Zadeh, L.A., QuantitativeFuzzy SemanticsInf.Sci.,3(1973), 159–176.MathSciNetCrossRefGoogle Scholar
  20. [20]
    Zadeh, L.A.The concept ofalinguistic variaile and its applicationto approximatereasoning I II III InLSci.,8(1975), 199–257, 301–357;9(1975), 43–80.Google Scholar
  21. [21]
    Zadeh, L.A.PRUF —a Meaning RepresentationLanguage for Natural Languagesht.J.Man-Mach.Stud.10(1978), 395–460.MathSciNetzbMATHCrossRefGoogle Scholar
  22. [22]
    Zadeh, L.A.A computational approach to fuzzy quantifiers in natural languagesComp. Math. with Applies 9(1983), 149–184.MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Vilém Novák
    • 1
  1. 1.Mining InstituteCzechoslovak Academy of SciencesOstrava-PorubaCzechoslovakia

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