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This study investigated the effects of semantic transparency of Chinese disyllabic compound words on Chinese as a second language (CSL) learners' incidental learning of word meanings in sentence-level reading and passage-level reading. The accuracy of the learners' lexical inferencing was compared among various types of words (transparent, semitransparent, and opaque words), different context lengths (sentence and passage contexts), and learners with different L1 backgrounds (with and without a Chinese character background in their L1s). In the study, ninety CSL adult learners were asked to infer the meanings of target words in the sentence context and the passage context. The results indicated that the effects of semantic transparency and context length on inferencing accuracy were significant, while the effect of L1 background was not. It was also found that there were significant interactions between transparency and context length as well as between transparency and L1 background. KeywordsSemantic transparency • Incidental learning of word meanings • Context length • L1 background • Chinese as a second language * Ming Tang

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The problem of word segmentation affects all aspects of Chinese language processing, including the development of text-to-speech synthesis systems. In synthesizing a Hong Kong Cantonese text, for example, words must be identified in order to model fusion of coda [p] with initial [h], and other similar effects that differentiate word-internal syllable boundaries from syllable edges that begin or end words. Accurate segmentation is necessary also for developing any list of words large enough to identify the wordinternal cross-syllable sequences that must be recorded to model such effects using concatenated synthesis units. This paper describes our use of the Segmentation Corpus to constrain such units.