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Entropy 2017, 19(5), 198;

Discovery of Kolmogorov Scaling in the Natural Language

Department of Physics and Astronomy, Sejong University, Seoul 143-747, Korea
Academic Editors: Maxim Raginsky and Raúl Alcaraz Martínez
Received: 16 February 2017 / Revised: 25 April 2017 / Accepted: 26 April 2017 / Published: 2 May 2017
(This article belongs to the Special Issue Information Theory in Machine Learning and Data Science)
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We consider the rate R and variance σ 2 of Shannon information in snippets of text based on word frequencies in the natural language. We empirically identify Kolmogorov’s scaling law in σ 2 k - 1 . 66 ± 0 . 12 (95% c.l.) as a function of k = 1 / N measured by word count N. This result highlights a potential association of information flow in snippets, analogous to energy cascade in turbulent eddies in fluids at high Reynolds numbers. We propose R and σ 2 as robust utility functions for objective ranking of concordances in efficient search for maximal information seamlessly across different languages and as a starting point for artificial attention. View Full-Text
Keywords: Shannon information; concordances; ranking; search; attention Shannon information; concordances; ranking; search; attention

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van Putten, M.H.P.M. Discovery of Kolmogorov Scaling in the Natural Language. Entropy 2017, 19, 198.

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