Zipf Distributions from Two-Stage Symbolic Processes: Stability Under Stochastic Lexical Filtering

Fuente: arXiv
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Autore principale: Berman, Vladimir
Natura: Preprint
Pubblicazione: 2025
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author Berman, Vladimir
author_facet Berman, Vladimir
contents Zipf's law in language lacks a definitive origin, debated across fields. This study explains Zipf-like behavior using geometric mechanisms without linguistic elements. The Full Combinatorial Word Model (FCWM) forms words from a finite alphabet, generating a geometric distribution of word lengths. Interacting exponential forces yield a power-law rank-frequency curve, determined by alphabet size and blank symbol probability. Simulations support predictions, matching English, Russian, and mixed-genre data. The symbolic model suggests Zipf-type laws arise from geometric constraints, not communicative efficiency.
format Preprint
id arxiv_https___arxiv_org_abs_2511_21060
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Zipf Distributions from Two-Stage Symbolic Processes: Stability Under Stochastic Lexical Filtering
Berman, Vladimir
Methodology
Computation and Language
Machine Learning
Zipf's law in language lacks a definitive origin, debated across fields. This study explains Zipf-like behavior using geometric mechanisms without linguistic elements. The Full Combinatorial Word Model (FCWM) forms words from a finite alphabet, generating a geometric distribution of word lengths. Interacting exponential forces yield a power-law rank-frequency curve, determined by alphabet size and blank symbol probability. Simulations support predictions, matching English, Russian, and mixed-genre data. The symbolic model suggests Zipf-type laws arise from geometric constraints, not communicative efficiency.
title Zipf Distributions from Two-Stage Symbolic Processes: Stability Under Stochastic Lexical Filtering
topic Methodology
Computation and Language
Machine Learning
url https://arxiv.org/abs/2511.21060