Hierarchical structures induce long-range dynamical correlations in written texts.

By Enrique Álvarez-Lacalle, Beate Dorow, Jean-Pierre Eckmann, Elisha Moses. 2006

88 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics, Computational Linguistics & Semantics, AI for Physical Sciences

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Citations in the corpus

On the origin of long-range correlations in texts (2012, neutral): “CxðtÞ ≔ hxjxjþti − hxjihxjþti ≃ t−β; 0 < β < 1; [2]”
Languages cool as they expand: Allometric scaling and the decreasing need for new words (2012, background): “Languages, like biological systems demonstrate economies of scale (b < 1) manifesting from a complex dependency structure that mimics a hierarchical “circulatory system” required by the organization of language [33, 41–45] and the limits of the efficiency of the speakers/writers who employ the words”
Long Range Dependence (2007, background): “A number of related topics is also discussed, including connections with non-stationary processes, with ergodic theory, self-similar processes and fractionally differenced processes, heavy tails and light tails, limit theorems and large deviations.”
Data Distributional Properties Drive Emergent In-Context Learning in Transformers (2022, background): “There are many possible ways to quantify or instantiate burstiness (e.g., Altmann et al., 2009; Alvarez-Lacalle et al., 2006; Lambiotte et al., 2013; Neuts, 2007; Sarkar et al., 2005; Serrano et al., 2009), but the ‘bursty’ sequences in our experiment were designed to reflect the within-context burs”
Towards the Quantification of the Semantic Information Encoded in Written Language (2009, background): “It has been argued that this is possible due to the presence of long-range memory in word sequences, which in turn creates organisational structures that go beyond the scope of sentences and paragraphs, and can extend for hundreds or thousands of words [1].”
Beyond Word Frequency: Bursts, Lulls, and Scaling in the Temporal Distributions of Words (2009, background): “This model does not reproduce the positive correlations between τj and τj+p [2, 6, 20], which are usually small (less than 20% for p = 1) but decay slowly with p (see Text S1, Correlation in {τj} ).”