Abstract
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 have demonstrated ambiguity advantages in both naming and lexical decision tasks. Although the predictive power of 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 the various contexts associated with a given word relate to each other. To explore these issues, we computed the contextual diversity (Adelman, Brown, & Quesada, Psychological Science, 17; 814–823, 2006) and semantic ambiguity (Hoffman, Lambon Ralph, & Rogers, Behavior Research Methods, 45; 718–730, 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 the 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 naming response times, suggesting that considering the substructure of the various contexts associated with a given word can provide a relatively fine scale of ambiguity information for a word. All of the context and ambiguity measures for 2,418 Chinese single-character words are provided as supplementary materials.
Highlights
Words that can be associated with multiple meanings are ambiguous, because their exact use varies depending on the immediate language context
We focused our attention on examining the relationships between semantic diversity (SemD), Semantic variability (SemVar), and other lexicalsemantic factors that have previously been shown to be important in naming including character frequency, number of strokes, consistency, imageability, and in particular semantic ambiguity rating (Chang et al, 2016; Lee et al, 2005; Liu et al, 2007)
When log Character frequency (CF) was included as a fixed effect into the baseline model termed Model 1, it resulted in a significant improvement of model fit, χ2(1) = 221.37, p
Summary
Words that can be associated with multiple meanings are ambiguous, because their exact use varies depending on the immediate language context. The findings of ambiguity advantage reported by some previous studies (Azuma & Van Orden, 1997; Borowsky & Masson, 1996; Kellas et al, 1988) used words that only differed in the number of senses but not always in the number of meanings When they considered the two types of ambiguity in lexical decision, Rodd, Gaskell, and Marslen-Wilson (2002) demonstrated that ambiguity between multiple senses could facilitate response latencies, whereas multiple meanings could prolong the latencies. For ambiguous words with few senses, relatively small attractors were developed for each unrelated semantic representation that could not effectively facilitate semantic activations; instead the competition between them resulted in an inhibition effect
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