@InProceedings{yeh-hsu-yeh:2016:NLPTEA2016,
  author    = {Yeh, Jui-Feng  and  Hsu, Tsung-Wei  and  Yeh, Chan-Kun},
  title     = {Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning},
  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     = {140--147},
  abstract  = {Mandarin is not simple language for foreigner. Even using Mandarin as the
	mother tongue, they have to spend more time to learn when they were child. The
	following issues are the reason why causes learning problem. First, the word is
	envolved by Hieroglyphic. So a character can express meanings independently,
	but become a word has another semantic. Second, the Mandarin's grammars have
	flexible rule and special usage. Therefore, the common grammatical errors can
	classify to missing, redundant, selection and disorder. In this paper, we
	proposed the structure of the Recurrent Neural Networks using Long Short-term
	memory (RNN-LSTM). It can detect the error type from the foreign learner
	writing. The features based on the word vector and part-of-speech vector. In
	the test data found that our method in the detection level of recall better
	than the others, even as high as 0.9755. That is because we give the
	possibility of greater choice in detecting errors.},
  url       = {http://aclweb.org/anthology/W16-4918}
}