@InProceedings{kaneko-sakaizawa-komachi:2017:I17-1,
  author    = {Kaneko, Masahiro  and  Sakaizawa, Yuya  and  Komachi, Mamoru},
  title     = {Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {40--48},
  abstract  = {In this study, we improve grammatical error detection by learning word
	embeddings that consider grammaticality and error patterns.
	Most existing algorithms for learning word embeddings usually model only the
	syntactic context of words so that classifiers treat erroneous and correct
	words as similar inputs.
	We address the problem of contextual information by considering learner errors.
	Specifically, we propose two models: one model that employs grammatical error
	patterns and another model that considers grammaticality of the target word.
	We determine grammaticality of n-gram sequence from the annotated error tags
	and extract grammatical error patterns for word embeddings from large-scale
	learner corpora.
	Experimental results show that a bidirectional long-short term memory model
	initialized by our word embeddings achieved the state-of-the-art accuracy by a
	large margin in an English grammatical error detection task on the First
	Certificate in English dataset.},
  url       = {http://www.aclweb.org/anthology/I17-1005}
}