What Are the Functional Orthographic Units in Chinese Word Recognition: The Stroke or the Stroke Pattern?

1996 ◽  
Vol 49 (4) ◽  
pp. 1024-1043 ◽  
Author(s):  
Y. P. Chen ◽  
D. A. Allport ◽  
J. C. Marshall
Keyword(s):  
Word Recognition ◽  
Chinese Word

Morpho-semantic processing in Chinese word recognition: An ERP study

2017 ◽  
Vol 49 (3) ◽  
pp. 296
Author(s):  
Simin ZHAO ◽  
Yan WU ◽  
Tianhong LI ◽  
Qingtong GUO
Keyword(s):  
Word Recognition ◽  
Semantic Processing ◽  
Chinese Word

scholarly journals Homophone density and phonological frequency in Chinese word recognition

2009 ◽  
Vol 24 (7-8) ◽  
pp. 967-982 ◽  
Author(s):  
Hsin-Chin Chen ◽  
Jyotsna Vaid ◽  
Jei-Tun Wu
Keyword(s):  
Word Recognition ◽  
Chinese Word

scholarly journals Punctuation as Implicit Annotations for Chinese Word Segmentation

2009 ◽  
Vol 35 (4) ◽  
pp. 505-512 ◽  
Author(s):  
Zhongguo Li ◽  
Maosong Sun
Keyword(s):  
Word Recognition ◽  
Word Segmentation ◽  
Chinese Word ◽  
Chinese Word Segmentation ◽  
Unknown Word

We present a Chinese word segmentation model learned from punctuation marks which are perfect word delimiters. The learning is aided by a manually segmented corpus. Our method is considerably more effective than previous methods in unknown word recognition. This is a step toward addressing one of the toughest problems in Chinese word segmentation.

Individual differences in Cantonese Chinese word recognition: Insights from the Chinese Lexicon Project

2020 ◽  
Vol 73 (4) ◽  
pp. 504-518 ◽  
Author(s):  
Rosemarie YH Lim ◽  
Melvin J Yap ◽  
Chi-Shing Tse
Keyword(s):  
Individual Differences ◽  
Word Recognition ◽  
Lexical Decision ◽  
Lexical Processing ◽  
Response Times ◽  
Recognition Performance ◽  
Processing System ◽  
Task Demands ◽  
Chinese Word ◽  
The Stability

The Chinese Lexicon Project is a repository of lexical decision data for 25,286 Cantonese Chinese two-character compound words. To create that repository, 594 participants responded to approximately 1,404 words and 1,404 nonwords over three sessions. Using the data in this repository, the present study examines the variability and reliability of Chinese lexical decision performance, along with the moderating influence of individual differences on lexical processing. We generally found high to very high within- and between-session reliabilities for mean response times, ex-Gaussian parameters, accuracy rates, and a composite proficiency measure tapping lexical processing fluency. Using linear mixed effects models, we also found reliable interactions between fluency and two lexical effects. Specifically, more fluent readers showed larger effects of word frequency and semantic transparency. These results attest to the stability of Chinese word recognition performance, and are most consistent with a flexible lexical processing system that adapts optimally to task demands.

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