Words, Concepts, and the Geometry of Analogy

TL;DR

This paper introduces a geometric framework for understanding word and concept relationships, especially analogies, using high-dimensional spaces to enable dynamic, context-aware analogy solving in language and cognition.

Contribution

It proposes a novel geometric approach to model analogies in language, integrating static and dynamic semantic spaces for improved interpretation.

Findings

Geometric conceptual spaces can effectively model word relationships.

Dynamic high-dimensional spaces enable online analogy solving.

Statistics can be interpreted within a geometric environment.

Abstract

This paper presents a geometric approach to the problem of modelling the relationship between words and concepts, focusing in particular on analogical phenomena in language and cognition. Grounded in recent theories regarding geometric conceptual spaces, we begin with an analysis of existing static distributional semantic models and move on to an exploration of a dynamic approach to using high dimensional spaces of word meaning to project subspaces where analogies can potentially be solved in an online, contextualised way. The crucial element of this analysis is the positioning of statistics in a geometric environment replete with opportunities for interpretation.