Predicting patterns of similarity among abstract semantic relations.
Predicting patterns of similarity among abstract semantic relations.
复制标题
DOI：
10.1037/xlm0001010
复制
发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Nicholas Ichien;Hongjing Lu;K. Holyoak
中科院分区：
文献类型：
--
作者：
Nicholas Ichien;Hongjing Lu;K. Holyoak
虽然基于分布语义的词义模型已被证明在预测人类对单个概念之间相似性的判断方面是有效的，但尚不清楚这些模型是否或如何扩展到解释概念之间关系的相似性判断。在这里，我们结合联合收割机的个体差异的方法与计算建模来预测人类判断的相似性之间的词对实例化的各种抽象语义关系（例如，对比、因果、部分-整体）。一项认知能力的测量预测了区分不同关系的能力的个体差异。人类模式的关系相似性的判断，无论是在组的水平和个人参与者，最好的预测模型，表示的词义的基础上分布语义作为其输入，并使用它们来学习一个明确的表示关系。这些研究结果表明，虽然抽象的语义关系的意义不直接编码在单个单词的意义，关系相似性的重要方面可以从分布语义。（PsycInfo数据库记录（c）2021阿帕，保留所有权利）。
Although models of word meanings based on distributional semantics have proved effective in predicting human judgments of similarity among individual concepts, it is less clear whether or how such models might be extended to account for judgments of similarity among relations between concepts. Here we combine an individual-differences approach with computational modeling to predict human judgments of similarity among word pairs instantiating a variety of abstract semantic relations (e.g., contrast, cause-effect, part-whole). A measure of cognitive capacity predicted individual differences in the ability to discriminate among distinct relations. The human pattern of relational similarity judgments, both at the group level and for individual participants, was best predicted by a model that takes representations of word meanings based on distributional semantics as its inputs and uses them to learn an explicit representation of relations. These findings indicate that although the meanings of abstract semantic relations are not directly coded in the meanings of individual words, important aspects of relational similarity can be derived from distributional semantics. (PsycInfo Database Record (c) 2021 APA, all rights reserved).