TITLE:
Quantifying Language Disparities: A Distance-Based Predictive Model Using Linguistic Tree Structures
AUTHORS:
Akshata Chavan, Nicole Lee, Rezza Moieni, Mary Legrand
KEYWORDS:
Linguistic Evolution, Language Disparity, Shortest Path Algorithms, Exponential Decay, Levenshtein Distance, Computational Linguistics, Quantitative Linguistics, Network Analysis
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.13 No.12,
December
31,
2025
ABSTRACT: Understanding the historical evolution and divergence of languages requires a quantitative framework for measuring their relationships. Traditional linguistic classifications provide hierarchical structures but lack numerical quantification of language disparity. This project addresses this gap by developing a computational model to quantify linguistic distance within a language tree. This study employs a graph-based model, utilising the Shortest Path Algorithm and Levenshtein Distance to determine the minimum distance between languages, capturing their ancestral and lexical relationships, respectively. This distance is then converted into a relationship score using an exponential decay function, reflecting the observed non-linear pattern of linguistic similarity. The resulting model provides a robust computational tool for understanding language disparity, moving beyond broad classifications to provide numerical insights into how closely languages are related, contributing to the study of language evolution.