Competition between Two Kinds of Correlations in Literary Texts
arXiv:physics/0402042 · doi:10.1103/PhysRevE.72.026140
Abstract
A theory of additive Markov chains with long-range memory is used for description of correlation properties of coarse-grained literary texts. The complex structure of the correlations in texts is revealed. Antipersistent correlations at small distances, L < 300, and persistent ones at L > 300 define this nontrivial structure. For some concrete examples of literary texts, the memory functions are obtained and their power-law behavior at long distances is disclosed. This property is shown to be a cause of self-similarity of texts with respect to the decimation procedure.
7 pages, 7 figures, Submitted to Physica A
References in corpus (7)
- Method for Generating Long-Range Correlations for Large Systems
- Global Persistence Exponent for Critical Dynamics
- Mobility Edge in Aperiodic Kronig-Penney Potentials with Correlated Disorder: Perturbative Approach
- Cyclization of a Polymer: A First Passage Problem for a Non-Markovian Process
- Phase-Transition in Binary Sequences with Long-Range Correlations
- Symbolic stochastic dynamical systems viewed as binary N-step Markov chains
- Can coarse-graining introduce long-range correlations in a symbolic sequence?
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