Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension

TL;DR

This paper presents a computational implementation of a metaphor comprehension model based on category theory, demonstrating improved performance in data fitting, systematicity, and novelty over existing algorithms.

Contribution

The authors simplified and verified a category-theoretic model of metaphor comprehension through computational algorithms and empirical evaluation.

Findings

Improved algorithm outperforms existing ones in data fitting.

Enhanced model achieves better systematicity in metaphor understanding.

Model demonstrates higher novelty in metaphor comprehension results.

Abstract

In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transformation (TINT) proposed by Fuyama et al. We simplified the algorithms implementing the model to be closer to the original theory and verified it through data fitting and simulations. The outputs of the algorithms are evaluated with three measures: data-fitting with experimental data, the systematicity of the metaphor comprehension result, and the novelty of the comprehension (i.e. the correspondence of the associative structure of the source and target of the metaphor). The improved algorithm outperformed the existing ones in all the three measures.