Similarities and Differences Between Chinese Two-Character Nouns and Verbs in Lexical Variables

Nouns and verbs are basic word classes in any language. Based on the 11 lexical variables (age of acquisition, concreteness, imageability, sensory experience arousal, emotion arousal, valence, familiarity, semantic transparency, number of strokes, number of meanings, and frequency) of Chinese two-character words from an existing dataset, the present study enriched the data by conducting a lexical decision task and a word class rating task (available at https://osf.io/x86bs/?view_only=0c0f5c3ae7ab423baa76d50bcb597cd2 ). We compared nouns with verbs in patterns of variable score distribution, variable correlation, and prediction of variables on lexical processing in lexical decision task. The results showed that nouns had higher scores in concreteness, imageability, sensory experience arousal, number of meanings, and valence and obtained lower scores in age of acquisition, frequency, emotion arousal, number of strokes, familiarity, and semantic transparency than verbs. Nouns were both similar to and different from verbs in patterns of feature correlations and feature prediction during lexical access. Skilled readers might be more sensitive to imageability, valence, and emotion arousal but less conscious of frequency of nouns than of verbs. Semantic variables accounted for a greater share of processing variances for nouns than for verbs in lexical decision task. Those results addressed the primary role of semantics in distinguishing nouns from verbs in the absence of syntactic demands. The broader implication is that semantics and grammar might co-evolve and jointly contribute to noun-verb distinctions, which still requires further verification in tasks with explicit syntactic demands. These findings further our understanding on Chinese word class.