Investigating the effects of semantic radical consistency in chinese character naming with a corpus-based measure.

Abstract

Semantic transparency refers to the degree to which the meaning of the whole word can be inferred from its constituents. For Chinese, semantic radicals generally carry information about the meanings of Chinese characters and, thus, can be used to reflect semantic transparency of Chinese characters. For those Chinese characters having the same semantic radicals (i.e., neighboring characters), their meanings are assumed to be semantically related to each other. However, to what extent those neighboring characters are close in their meanings remains unclear. A conventional crowdsource approach could provide a coarse measure of semantic relationships between semantic neighbors. However, those approaches are generally limited to a small sample size of characters. Here, we proposed a corpus-based measure of semantic transparency, termed semantic radical consistency (SRC). Specifically, we utilized the Word2Vec models to construct a Chinese semantic space and quantified the SRC for 3,423 characters. To evaluate the SRC, we first conducted linear mixed-effect modeling analyses to verify the explanatory power of SRC on a large-scale Chinese character naming reaction times. Second, we investigated the SRC effect by conducting a word naming task based on traditional factorial designs. Both the linear mixed-effect modeling and factorial naming results demonstrated that SRC was a unique and reliable variable to account for the variance in traditional Chinese character naming reaction times. The results indicated this innovative, corpus-derived SRC was able to effectively reflect the semantic transparency level by measuring semantic distances among characters in the same semantic radical category. Further investigations on the interaction between SRC and phonetic radical consistency demonstrated the cooperative nature between phonological and semantic reading pathways. (PsycInfo Database Record (c) 2025 APA, all rights reserved).