Entropic Analysis of Garhwali Text

Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


In the present study, a systematic statistical analysis has been performed by the use of words in continuous Garhwali speech corpus. The words of Garhwali in continuous speech corpus are taken from different sources of Garhwali, viz., Newspapers, storybooks, poems, lyrics of songs and magazines, and it showed that there is a quantitative relation between the role of content words in Garhwali and the Shannon information entropy [S] defined by the probability distribution. So far, very few researches have been conducted in Garhwali language. There is no previous knowledge about the syntactic structure of Garhwali language. We have taken finite continuous corpus of Garhwali language. The occurrences of words (frequency) are almost an inverse power law functions, i.e. Zipf’s law and very close to 1.


Entropy Garhwali Rank Frequency 


  1. 1.
    Lawnik M, Shannon’s entropy in literary works and their translations (web:
  2. 2.
    Nigam K, Lafferty J, McCallum A (1999) Using maximum entropy for text classification. In: IJCAI-99 workshop on machine learning for information filtering, vol 1Google Scholar
  3. 3.
    Papadimitriou C, Karamanos K, Diakonos FK, Constantoudis V, Papageorgiou H (2010) Entropy analysis of natural language written texts. Physica A 389(16):3260–3266CrossRefGoogle Scholar
  4. 4.
    Shannon Claude E (1948) A mathematical theory of communications. Bell Syst Tech J 27:379–423MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kalimeri M, Constantoudis V, Papadimitriou C, Karamanos K, Diakonos FK, Papageorgiou H (2012) Entropy analysis of word-length series of natural language texts: Effects of text language and genre. Int J Bifurcat Chaos 22(09):1250223CrossRefGoogle Scholar
  6. 6.
    Ebeling Werner, Pöschel Thorsten (1994) Entropy and long-range correlations in literary English. EPL (Europhysics Letters) 26(4):241CrossRefGoogle Scholar
  7. 7.
    Kalimeri M, Constantoudis V, Papadimitriou C, Karamanos K, Diakonos FK, Papageorgiou H (2015) Word-length entropies and correlations of natural language written texts. J Quant Linguist 22(2):101–118CrossRefGoogle Scholar
  8. 8.
    Montemurro MA, Zanette DH (2002) Entropic analysis of the role of words in literary texts. Adv Complex Syst 5(01):7–17CrossRefGoogle Scholar
  9. 9.
    Ospanova R (2013) Calculating information entropy of language texts. World Appl Sci J 22(1):41–45Google Scholar
  10. 10.
    Zipf GK (1949) Human behaviour and the principal of least effort, reading. Addison-Wesley Publishing Co., MAGoogle Scholar
  11. 11.
    Cancho RFI, Sole´ RV (2002) Least effort and the origins of scaling in human language. Proceedings of the national academy of sciences of the United States of America, vol 100, pp 788–791Google Scholar
  12. 12.
    Devadoss S, Luckstead J, Danforth D, Akhundjanov S (2016) The power law distribution for lower tail cities in India. Physica A 15(442):193–196CrossRefGoogle Scholar
  13. 13.
    Riyal MK, Rajput NK, Khanduri VP, Rawat L (2016) Rank-frequency analysis of characters in Garhwali text: emergence of Zipf’s law. Curr Sci 110(3):429–434CrossRefGoogle Scholar
  14. 14.
    Manin DY (2009) Mandelbrot’s model for Zipf’s law: can Mandelbrot’s model explain Zipf’s law for language? J Quant Linguist 16(3):274–285MathSciNetCrossRefGoogle Scholar
  15. 15.
    Montemurro Marcelo A (2001) Beyond the Zipf-Mandelbrot law in quantitative linguistics. Physica A 300(3-4):567–578CrossRefGoogle Scholar
  16. 16.
  17. 17.

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© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Department of PhysicsGovernment Post Graduate CollegeKotdwara, Pauri GarhwalIndia
  2. 2.Department of PhysicsV. A. Government Degree CollegeAtrauly District AligarhIndia
  3. 3.Physics Lecturer, Government Senior Secondary Boys SchoolPhagwaraIndia

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