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Text mixing shapes the anatomy of rank-frequency distributions
Journal article   Open access

Text mixing shapes the anatomy of rank-frequency distributions

Jake Ryland Williams, James P Bagrow, Christopher M Danforth and Peter Sheridan Dodds
Physical review. E, Statistical, nonlinear, and soft matter physics, v 91(5), pp 052811-052811
May 2015
DOI: https://doi.org/10.1103/PhysRevE.91.052811
PMID: 26066216
url
https://doi.org/10.1103/physreve.91.052811View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Natural languages are full of rules and exceptions. One of the most famous quantitative rules is Zipf's law, which states that the frequency of occurrence of a word is approximately inversely proportional to its rank. Though this "law" of ranks has been found to hold across disparate texts and forms of data, analyses of increasingly large corpora since the late 1990s have revealed the existence of two scaling regimes. These regimes have thus far been explained by a hypothesis suggesting a separability of languages into core and noncore lexica. Here we present and defend an alternative hypothesis that the two scaling regimes result from the act of aggregating texts. We observe that text mixing leads to an effective decay of word introduction, which we show provides accurate predictions of the location and severity of breaks in scaling. Upon examining large corpora from 10 languages in the Project Gutenberg eBooks collection, we find emphatic empirical support for the universality of our claim.

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Physics, Fluids & Plasmas
Physics, Mathematical
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