A Computational Model to Disentangle Semantic Information Embedded in Word Association Norms

TL;DR

This paper introduces a computational algorithm that leverages the topology of semantic networks to distinguish feature-based relationships from association-based ones, enhancing understanding of semantic structures.

Contribution

The novel algorithm disentangles feature-based semantic relationships from association networks using network topology analysis, providing a new method for semantic network analysis.

Findings

Successfully separates feature-based from association-based relationships

Produces relationships similar to feature production norms

Enhances semantic network analysis techniques

Abstract

Two well-known databases of semantic relationships between pairs of words used in psycholinguistics, feature-based and association-based, are studied as complex networks. We propose an algorithm to disentangle feature based relationships from free association semantic networks. The algorithm uses the rich topology of the free association semantic network to produce a new set of relationships between words similar to those observed in feature production norms.