Modeling the Structure and Dynamics of the Consonant Inventories: A Complex Network Approach

TL;DR

This paper models the structure and evolution of consonant inventories across languages using complex network theory, revealing power-law distributions and high clustering, and proposes models that mimic language development and can aid NLP.

Contribution

It introduces four refined network models that replicate linguistic properties and topological features of consonant inventories, linking network growth to language acquisition.

Findings

Consonant occurrence and co-occurrence follow power-law distributions.

The co-occurrence network has a high clustering coefficient.

Models successfully replicate linguistic and topological properties.

Abstract

We study the self-organization of the consonant inventories through a complex network approach. We observe that the distribution of occurrence as well as cooccurrence of the consonants across languages follow a power-law behavior. The co-occurrence network of consonants exhibits a high clustering coefficient. We propose four novel synthesis models for these networks (each of which is a refinement of the earlier) so as to successively match with higher accuracy (a) the above mentioned topological properties as well as (b) the linguistic property of feature economy exhibited by the consonant inventories. We conclude by arguing that a possible interpretation of this mechanism of network growth is the process of child language acquisition. Such models essentially increase our understanding of the structure of languages that is influenced by their evolutionary dynamics and this, in turn, can be extremely useful for building future NLP applications.