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Language time series analysis

Author

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  • Kosmidis, Kosmas
  • Kalampokis, Alkiviadis
  • Argyrakis, Panos

Abstract

We use the detrended fluctuation analysis (DFA) and the Grassberger–Proccacia analysis (GP) methods in order to study language characteristics. Despite that we construct our signals using only word lengths or word frequencies, excluding in this way huge amount of information from language, the application of GP analysis indicates that linguistic signals may be considered as the manifestation of a complex system of high dimensionality, different from random signals or systems of low dimensionality such as the Earth climate. The DFA method is additionally able to distinguish a natural language signal from a computer code signal. This last result may be useful in the field of cryptography.

Suggested Citation

  • Kosmidis, Kosmas & Kalampokis, Alkiviadis & Argyrakis, Panos, 2006. "Language time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 808-816.
    • Handle: RePEc:eee:phsmap:v:370:y:2006:i:2:p:808-816
    DOI: 10.1016/j.physa.2006.02.042
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    Citations

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    Cited by:

    1. Rodriguez, E. & Aguilar-Cornejo, M. & Femat, R. & Alvarez-Ramirez, J., 2014. "Scale and time dependence of serial correlations in word-length time series of written texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 378-386.
    2. Ausloos, M., 2012. "Measuring complexity with multifractals in texts. Translation effects," Chaos, Solitons & Fractals, Elsevier, vol. 45(11), pages 1349-1357.

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