Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension

arXiv cs.CL / 4/14/2026

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Key Points

The paper presents a computational implementation of a metaphor comprehension model grounded in the theory of indeterminate natural transformation (TINT) by Fuyama et al.
The authors simplify and refine the algorithms to better match the original theoretical framework.
They validate the approach via data fitting and simulation experiments, using evaluation criteria focused on accuracy, systematicity, and “novelty” defined by source–target associative structure correspondence.
The improved algorithm reportedly outperforms existing implementations across all three evaluation measures.

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.