Discovering Global Patterns in Linguistic Networks through Spectral Analysis: A Case Study of the Consonant Inventories

TL;DR

This paper applies spectral analysis to linguistic networks, specifically PhoNet, to uncover global patterns and principles governing consonant inventories, offering a novel structural approach to language analysis.

Contribution

It introduces spectral analysis as a new method for studying the global structure of linguistic networks, demonstrated through a case study on consonant co-occurrence networks.

Findings

Revealed natural linguistic principles in consonant inventories

Quantified the importance of different structural features

Demonstrated the effectiveness of spectral analysis for language networks

Abstract

Recent research has shown that language and the socio-cognitive phenomena associated with it can be aptly modeled and visualized through networks of linguistic entities. However, most of the existing works on linguistic networks focus only on the local properties of the networks. This study is an attempt to analyze the structure of languages via a purely structural technique, namely spectral analysis, which is ideally suited for discovering the global correlations in a network. Application of this technique to PhoNet, the co-occurrence network of consonants, not only reveals several natural linguistic principles governing the structure of the consonant inventories, but is also able to quantify their relative importance. We believe that this powerful technique can be successfully applied, in general, to study the structure of natural languages.