@InProceedings{shiue-huang-chen:2017:Short,
  author    = {Shiue, Yow-Ting  and  Huang, Hen-Hsen  and  Chen, Hsin-Hsi},
  title     = {Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {404--410},
  abstract  = {Selecting appropriate words to compose a sentence is one common problem faced
	by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM
	sequence labeling models and explore various features to detect word usage
	errors in Chinese sentences. By combining CWINDOW word embedding features and
	POS information, the best bidirectional LSTM model achieves accuracy 0.5138 and
	MRR 0.6789 on the HSK dataset. For 80.79% of the test data, the model ranks the
	ground-truth within the top two at position level.},
  url       = {http://aclweb.org/anthology/P17-2064}
}