Evidence map›Paper›PMID 42236992›Full record

ArticleBehavior research methods2026

Semantic diversity in Chinese words: Exploring its role in word recognition, lexical ambiguity, character complexity, and semantic alignment.

Yufeng Liu, Shifa Chen, Yi Yang

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Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 authors.

Yufeng LiuDepartment of English Linguistics, Ocean University of China, Qingdao, China. liuyufeng6126@stu.ouc.edu.cn.ORCID https://orcid.org/0009-0009-1593-6753
Shifa ChenDepartment of English Linguistics, Ocean University of China, Qingdao, China. chenshifa99@ouc.edu.cn.
Yi YangDepartment of Chinese Language and Literature, Southwest Jiaotong University, Chengdu, China. yangyi_9811@163.com.

Funding

National Social Science Fund of China 21BYY114
6 · The paper itself

Abstract

Semantic diversity (SemD) quantifies how (dis)similar a word's various contextual usages are across linguistic experiences. While extensively validated in English, its application to Chinese-a morpho-syllabic language where most words consist of multiple characters-remains unexplored. Here, we present the first comprehensive SemD database for 25,460 simplified Chinese words spanning one-to-ten characters using context vectors from latent semantic analysis. We evaluate its cognitive validity through five lenses. First, Chinese SemD exhibits systematic relationships with dictionary-based ambiguity, human ratings, and key psycholinguistic variables. Crucially, unscaled SemD shows stronger associations with all factors than scaled variants. Second, higher SemD facilitates recognition speed and accuracy beyond baseline predictors in mega-study lexical decision, with a stronger benefit for low-frequency words-replicating English findings in an ideographic system. Third, both lexical ambiguity types-polysemy and homonymy-positively predict SemD, indicating that words with multiple related senses or unrelated meanings are associated with more diverse contextual usage. However, behavioral and SemD-based analyses convergingly show that only polysemes demonstrate a processing advantage and higher SemD relative to unambiguous controls, whereas homonyms do not differ from controls on either measure. Fourth, words with more characters exhibit lower SemD, extending the Mutual Annotation and Constraint account whereby additional morphemic cues narrow Chinese words' interpretation. Fifth, cross-linguistic analyses demonstrate that English-Chinese translation equivalents with higher semantic alignment exhibit lower mean SemD and smaller between-language SemD disparities. These findings establish SemD as a psychologically meaningful measure of Chinese lexical ambiguity that bridges distributional, morphological, and cross-linguistic perspectives on the mental lexicon.

Indexed as

LanguagePsycholinguisticsRecognition, PsychologySemanticsHumansChinese wordsDistributional semanticsLatent semantic analysisLexical ambiguitySemantic diversity

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