Semantic Automatic Error-Detecting for Chinese Text Based on Semantic Dependency Relationship

Author(s):  
Jiayuan Li ◽  
Yangsen Zhang ◽  
Jinjin Zhu ◽  
Zewei Zhang
Author(s):  
CHUEN-MIN HUANG ◽  
MEI-CHEN WU ◽  
CHING-CHE CHANG

Misspelling and misconception resulting from similar pronunciation appears frequently in Chinese texts. Without double check-up, this situation will be getting worse even with the help of Chinese input editor. It is hoped that the quality of Chinese writing would be enhanced if an effective automatic error detection and correction mechanism is embedded in text editor. Therefore, the burden of manpower to proofread shall be released. Until recently, researches in automatic error detection and correction of Chinese text have undergone many challenges and suffered from bad performance compared with that of Western text. In view of the prominent phenomenon in Chinese writing problem, this study proposes a learning model based on Chinese phonemic alphabets. The experimental results demonstrate that this model is effective in finding out misspellings and further improves detection and correction rate.


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