@InProceedings{chou-EtAl:2016:NLPTEA2016,
  author    = {Chou, Wei-Chieh  and  Lin, Chin-Kui  and  Liao, Yuan-Fu  and  Wang, Yih-Ru},
  title     = {Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection},
  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     = {73--81},
  abstract  = {This paper discusses how to adapt two new word embedding features to build a
	more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist
	Chinese foreign learners (CFLs) in improving their written essays. The major
	idea is to apply word order sensitive Word2Vec approaches including (1)
	structured skip-gram and (2) continuous window (CWindow) models, because they
	are more suitable for solving syntax-based problems. The proposed new features
	were evaluated on the Test of Chinese as a Foreign Language (TOCFL) learner
	database provided by NLP-TEA-3\&CGED shared task. Experimental results showed
	that the new features did work better than the traditional word order
	insensitive Word2Vec approaches. Moreover, according to the official evaluation
	results, our system achieved the lowest (0.1362) false positive (FA) and the
	highest precision rates in all three measurements.},
  url       = {http://aclweb.org/anthology/W16-4910}
}