Predicting raters' transparency judgments of English and Chinese morphological constituents using latent semantic analysis

Hong Kong Chinese character psycholinguistic norms: ratings of 4376 single Chinese characters on semantic radical transparency, age-of-acquisition, familiarity, imageability, and concreteness

Several norms of psycholinguistic features of Chinese characters exist in Mandarin Chinese, but only a few are available in Cantonese or in the traditional script, and none includes semantic radical transparency ratings. This study presents subjective ratings of age-of-acquisition (AoA), familiarity, imageability, concreteness, and semantic radical transparency in 4376 Chinese characters. The single Chinese characters were rated individually on the five dimensions by 20 native Cantonese speakers in Hong Kong to form the Hong Kong Chinese Character Psycholinguistic Norms (HKCCPN). The split-half reliability and intra-class correlations testified to the high internal reliability of the ratings. Their convergent and discriminant patterns in relations to other psycholinguistic measures echoed previous findings reported on Chinese. There were high correlations for semantic radical transparency, imageability and concreteness, and moderate-to-high correlations for AoA and familiarity among subsets of items that had been collected in previous studies. Concurrent validity analyses showed convergence in predicting behavioral response times in various tasks (lexical decision, naming, and writing-to-dictation) when compared with other Chinese character databases. High predictive validity was shown in writing-to-dictation data from an independent sample of 20 native Cantonese speakers. Several objective psycholinguistic measures (character frequency, stroke number, number of words formed, number of homophones and number of meanings) were included in this database to facilitate its use. These new ratings extend the currently available norms in language and reading research in Cantonese Chinese for researchers, clinicians, and educators, as well as provide them with a wider choice of stimuli.

The role of semantic transparency in visual word recognition of compound words: A megastudy approach

Previous studies on visual word recognition of compound words have provided evidence for the influence of lexical properties (e.g., length, frequency) and semantic transparency (the degree of relatedness in meaning between a compound word and its constituents) in morphological processing (e.g., to what extent is doorbell influenced by door and bell?). However, a number of questions in this domain, which are difficult to address with the available methodological resources, are still unresolved. We collected semantic transparency scores for 2,861 compound words at the constituent level (i.e., how strongly the overall meaning of a compound word is related to that of each constituent) and analyzed their effects on speeded pronunciation and lexical decision performance for the compound words using the English Lexicon Project (http://elexicon.wustl.edu) data. The results from both tasks indicated that our human-judged semantic transparency ratings for both the first and second constituents play a significant role in compound word processing. Moreover, additional analyses indicated that the human-judged semantic transparency scores at the constituent level accounted for more variance in compound word recognition performance than did either whole-word semantic transparency scores or corpus-based semantic distance scores.

LADEC: The Large Database of English Compounds

The Large Database of English Compounds (LADEC) consists of over 8,000 English words that can be parsed into two constituents that are free morphemes, making it the largest existing database specifically for use in research on compound words. Both monomorphemic (e.g., wheel) and multimorphemic (e.g., teacher) constituents were used. The items were selected from a range of sources, including CELEX, the English Lexicon Project, the British Lexicon Project, the British National Corpus, and Wordnet, and were hand-coded as compounds (e.g., snowball). Participants rated each compound in terms of how predictable its meaning is from its parts, as well as the extent to which each constituent retains its meaning in the compound. In addition, we obtained linguistic characteristics that might influence compound processing (e.g., frequency, family size, and bigram frequency). To show the usefulness of the database in investigating compound processing, we conducted a number of analyses that showed that compound processing is consistently affected by semantic transparency, as well as by many of the other variables included in LADEC. We also showed that the effects of the variables associated with the two constituents are not symmetric. In short, LADEC provides the opportunity for researchers to investigate a number of questions about compounds that have not been possible to investigate in the past, due to the lack of sufficiently large and robust datasets. In addition to directly allowing researchers to test hypotheses using the information included in LADEC, the database will contribute to future compound research by allowing better stimulus selection and matching.

Semantic ambiguity effects on traditional Chinese character naming: A corpus-based approach

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.