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arXiv · 2405.06321

Correlation Dimension of Natural Language in a Statistical Manifold

Xin Du·Kumiko Tanaka-Ishii
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Abstract

The correlation dimension of natural language is measured by applying the Grassberger-Procaccia algorithm to high-dimensional sequences produced by a large-scale language model. This method, previously studied only in a Euclidean space, is reformulated in a statistical manifold via the Fisher-Rao distance. Language exhibits a multifractal, with global self-similarity and a universal dimension around 6.5, which is smaller than those of simple discrete random sequences and larger than that of a Barab\'asi-Albert process. Long memory is the key to producing self-similarity. Our method is applicable to any probabilistic model of real-world discrete sequences, and we show an application to music data.

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Xin Du, Kumiko Tanaka-Ishii. 2024-05-10. Correlation Dimension of Natural Language in a Statistical Manifold. https://doi.org/10.1103/physrevresearch.6.l022028

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