@InProceedings{chen-tsai-lin:2016:NLPTEA2016,
  author    = {Chen, Shao-Heng  and  Tsai, Yu-Lin  and  Lin, Chuan-Jie},
  title     = {Generating and Scoring Correction Candidates in Chinese Grammatical Error Diagnosis},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {131--139},
  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.},
  url       = {http://aclweb.org/anthology/W16-4917}
}