Generating and Scoring Correction Candidates in Chinese Grammatical Error Diagnosis

Shao-Heng Chen, Yu-Lin Tsai, Chuan-Jie Lin


Abstract
Grammatical error diagnosis is an essential part in a language-learning tutoring system. Based on the data sets of Chinese grammar error detection tasks, we proposed a system which measures the likelihood of correction candidates generated by deleting or inserting characters or words, moving substrings to different positions, substituting prepositions with other prepositions, or substituting words with their synonyms or similar strings. Sentence likelihood is measured based on the frequencies of substrings from the space-removed version of Google n-grams. The evaluation on the training set shows that Missing-related and Selection-related candidate generation methods have promising performance. Our final system achieved a precision of 30.28% and a recall of 62.85% in the identification level evaluated on the test set.
Anthology ID:
W16-4917
Volume:
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Hsin-Hsi Chen, Yuen-Hsien Tseng, Vincent Ng, Xiaofei Lu
Venue:
NLP-TEA
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
131–139
Language:
URL:
https://aclanthology.org/W16-4917
DOI:
Bibkey:
Cite (ACL):
Shao-Heng Chen, Yu-Lin Tsai, and Chuan-Jie Lin. 2016. Generating and Scoring Correction Candidates in Chinese Grammatical Error Diagnosis. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 131–139, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Generating and Scoring Correction Candidates in Chinese Grammatical Error Diagnosis (Chen et al., NLP-TEA 2016)
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PDF:
https://aclanthology.org/W16-4917.pdf