The morphospace of language networks

TL;DR

This paper analyzes the landscape of possible language networks using a generic communication model, revealing a complex morphospace and positioning real languages within it, highlighting simplicity in human language structure.

Contribution

It provides the first systematic analysis of the landscape of language networks and empirically locates real languages within this theoretical framework.

Findings

Language networks form a complex and heterogeneous morphospace.

Real languages occupy a surprisingly simple region unless particles are included.

The study refines the least effort language theory with empirical data.

Abstract

Language can be described as a network of interacting objects with different qualitative properties and complexity. These networks include semantic, syntactic, or phonological levels and have been found to provide a new picture of language complexity and its evolution. A general approach considers language from an information theory perspective that incorporates a speaker, a hearer, and a noisy channel. The later is often encoded in a matrix connecting the signals used for communication with meanings to be found in the real world. Most studies of language evolution deal in a way or another with such theoretical contraption and explore the outcome of diverse forms of selection on the communication matrix that somewhat optimizes communication. This framework naturally introduces networks mediating the communicating agents, but no systematic analysis of the underlying landscape of possible language graphs has been developed. Here we present a detailed analysis of network properties on a generic model of a communication code, which reveals a rather complex and heterogeneous morphospace of language networks. Additionally, we use curated data of English words to locate and evaluate real languages within this language morphospace. Our findings indicate a surprisingly simple structure in human language unless particles are introduced in the vocabulary, with the ability of naming any other concept. These results refine and for the first time complement with empirical data a lasting theoretical tradition around the framework of \emph{least effort language}.