@InProceedings{yang-EtAl:2016:NLPTEA2016,
  author    = {Yang, Jinnan  and  Peng, Bo  and  Wang, Jin  and  Zhang, Jixian  and  Zhang, Xuejie},
  title     = {Chinese Grammatical Error Diagnosis Using Single Word Embedding},
  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     = {155--161},
  abstract  = {Abstract
	Automatic grammatical error detection for Chinese has been a big challenge for
	NLP researchers. Due to the formal and strict grammar rules in Chinese, it is
	hard for foreign students to master Chinese. A computer-assisted learning tool
	which can automatically detect and correct Chinese grammatical errors is
	necessary for those foreign students. Some of the previous works have sought to
	identify Chinese grammatical errors using template- and learning-based methods.
	In contrast, this study introduced convolutional neural network (CNN) and
	long-short term memory (LSTM) for the shared task of Chinese Grammatical Error
	Diagnosis (CGED). Different from traditional word-based embedding, single word
	embedding was used as input of CNN and LSTM. The proposed single word embedding
	can capture both semantic and syntactic information to detect those four type
	grammatical error. In experimental evaluation, the recall and f1-score of our
	submitted results Run1 of the TOCFL testing data ranked the fourth place in all
	submissions in detection-level.},
  url       = {http://aclweb.org/anthology/W16-4920}
}