用计算模型实现隐喻理解理论，提升准确性和创新性。
Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension
- 基于范畴论隐喻理论构建简化算法，贴近原理论
- 三项评估指标均优于现有方法，包括拟合度与系统性
- 适合认知科学与人工智能交叉研究者参考
本研究开发了一种基于富山等人提出的不确定自然变换理论（TINT）的隐喻理解计算模型。我们对实现该模型的算法进行了简化，使其更贴近原始理论，并通过数据拟合与模拟验证了其有效性。算法输出通过三个指标评估：与实验数据的拟合度、隐喻理解结果的系统性，以及理解结果的新颖性（即源域与目标域关联结构的一致性）。改进后的算法在所有三项指标上均优于现有方法。
原文摘要 · Abstract (English)
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.
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