Words are considered semantically ambiguous if they have more than one meaning and can be used in multiple contexts. A number of recent studies have provided objective ambiguity measures by using a corpus-based approach and demonstrated ambiguity advantage in both naming and lexical decision tasks. Although the predictive power of the objective ambiguity measures has been examined in several alphabetic language systems, the effects in logographic languages remain unclear. Moreover, most ambiguity measures do not explicitly address how various contexts associated with a given word relate to each other. To explore these issues, we computed contextual diversity (Adelman et al. 2006) and semantic ambiguity (Hoffman et al. 2013) of traditional Chinese single-character words based on the Academia Sinica Balanced Corpus, where contextual diversity was used to evaluate the present semantic space. We then derived a novel ambiguity measure, namely semantic variability, by computing distance properties of the distinct clusters grouped by the contexts that contained a given word. We demonstrated that semantic variability was superior to semantic diversity in accounting for the variance in the naming RTs, suggesting that considering the substructure of various contexts associated with a given word can provide a relatively fine scale of ambiguity information for a word. All the context and ambiguity measures for 2,418 Chinese single-character words are provided as supplementary materials.