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−γP(k)∼k-γ, with two regimes, which are characterized by the exponents γs≈1.7γs≈1.7 (at short degree scales) and γl≈1.3γl≈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.
Guzmán-Vargas, Lev; Obregón-Quintana, Bibiana; Aguilar-Velázquez, Daniel; Hernández-Pérez, Ricardo; and Liebovitch, Larry S., "Word-Length Correlations and Memory in Large Texts: A Visibility Network Analysis" (2015). CUNY Academic Works.