Word-length correlations and memory in large texts: A visibility network analysis

Lev Guzmán-Vargas, Bibiana Obregón-Quintana, Daniel Aguilar-Velázquez, Ricardo Hernández-Pérez, Larry S. Liebovitch

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Abstract

© 2015 by the authors. We study the correlation properties of word lengths in large texts from 30 ebooks in the English language from the Gutenberg Project (www.gutenberg.org) using the natural visibility graph method (NVG). NVG converts a time series into a graph and then analyzes its graph properties. First, the original sequence of words is transformed into a sequence of values containing the length of each word, and then, it is integrated. Next, we apply the NVG to the integrated word-length series and construct the network. We show that the degree distribution of that network follows a power law, P(k) k-γ, with two regimes, which are characterized by the exponents γs≈1.7 (at short degree scales) and γl≈ 1.3 (at large degree scales). This suggests that word lengths are much more strongly correlated at large distances between words than at short distances between words. That finding is also supported by the detrended fluctuation analysis (DFA) and recurrence time distribution. These results provide new information about the universal characteristics of the structure of written texts beyond that given by word frequencies.
Original languageAmerican English
Pages (from-to)7798-7810
Number of pages13
JournalEntropy
DOIs
StatePublished - 1 Jan 2015

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Guzmán-Vargas, L., Obregón-Quintana, B., Aguilar-Velázquez, D., Hernández-Pérez, R., & Liebovitch, L. S. (2015). Word-length correlations and memory in large texts: A visibility network analysis. Entropy, 7798-7810. https://doi.org/10.3390/e17117798
Guzmán-Vargas, Lev ; Obregón-Quintana, Bibiana ; Aguilar-Velázquez, Daniel ; Hernández-Pérez, Ricardo ; Liebovitch, Larry S. / Word-length correlations and memory in large texts: A visibility network analysis. In: Entropy. 2015 ; pp. 7798-7810.
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Guzmán-Vargas, L, Obregón-Quintana, B, Aguilar-Velázquez, D, Hernández-Pérez, R & Liebovitch, LS 2015, 'Word-length correlations and memory in large texts: A visibility network analysis', Entropy, pp. 7798-7810. https://doi.org/10.3390/e17117798

Word-length correlations and memory in large texts: A visibility network analysis. / Guzmán-Vargas, Lev; Obregón-Quintana, Bibiana; Aguilar-Velázquez, Daniel; Hernández-Pérez, Ricardo; Liebovitch, Larry S.

In: Entropy, 01.01.2015, p. 7798-7810.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Word-length correlations and memory in large texts: A visibility network analysis

AU - Guzmán-Vargas, Lev

AU - Obregón-Quintana, Bibiana

AU - Aguilar-Velázquez, Daniel

AU - Hernández-Pérez, Ricardo

AU - Liebovitch, Larry S.

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Y1 - 2015/1/1

N2 - © 2015 by the authors. We study the correlation properties of word lengths in large texts from 30 ebooks in the English language from the Gutenberg Project (www.gutenberg.org) using the natural visibility graph method (NVG). NVG converts a time series into a graph and then analyzes its graph properties. First, the original sequence of words is transformed into a sequence of values containing the length of each word, and then, it is integrated. Next, we apply the NVG to the integrated word-length series and construct the network. We show that the degree distribution of that network follows a power law, P(k) k-γ, with two regimes, which are characterized by the exponents γs≈1.7 (at short degree scales) and γl≈ 1.3 (at large degree scales). This suggests that word lengths are much more strongly correlated at large distances between words than at short distances between words. That finding is also supported by the detrended fluctuation analysis (DFA) and recurrence time distribution. These results provide new information about the universal characteristics of the structure of written texts beyond that given by word frequencies.

AB - © 2015 by the authors. We study the correlation properties of word lengths in large texts from 30 ebooks in the English language from the Gutenberg Project (www.gutenberg.org) using the natural visibility graph method (NVG). NVG converts a time series into a graph and then analyzes its graph properties. First, the original sequence of words is transformed into a sequence of values containing the length of each word, and then, it is integrated. Next, we apply the NVG to the integrated word-length series and construct the network. We show that the degree distribution of that network follows a power law, P(k) k-γ, with two regimes, which are characterized by the exponents γs≈1.7 (at short degree scales) and γl≈ 1.3 (at large degree scales). This suggests that word lengths are much more strongly correlated at large distances between words than at short distances between words. That finding is also supported by the detrended fluctuation analysis (DFA) and recurrence time distribution. These results provide new information about the universal characteristics of the structure of written texts beyond that given by word frequencies.

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Guzmán-Vargas L, Obregón-Quintana B, Aguilar-Velázquez D, Hernández-Pérez R, Liebovitch LS. Word-length correlations and memory in large texts: A visibility network analysis. Entropy. 2015 Jan 1;7798-7810. https://doi.org/10.3390/e17117798