@InProceedings{yang-EtAl:2017:I17-4,
  author    = {yang, Yi  and  Xie, Pengjun  and  tao, Jun  and  xu, Guangwei  and  li, Linlin  and  luo, Si},
  title     = {Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {41--46},
  abstract  = {This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1
	Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types
	of grammatical errors which are re- dundant words (R), missing words (M), bad
	word selection (S) and disordered words (W). We treat the task as a sequence
	tagging problem and design some hand- craft features to solve it. Our system is
	mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to
	improve the performance. At the identifi- cation level and the position level
	our sys- tem gets the highest F1 scores. At the posi- tion level, which is the
	most difficult level, we perform best on all metrics.},
  url       = {http://www.aclweb.org/anthology/I17-4006}
}