@InProceedings{fu-EtAl:2018:NLPTEA,
  author    = {Fu, Ruiji  and  Pei, Zhengqi  and  Gong, Jiefu  and  Song, Wei  and  Teng, Dechuan  and  Che, Wanxiang  and  Wang, Shijin  and  Hu, Guoping  and  Liu, Ting},
  title     = {Chinese Grammatical Error Diagnosis using Statistical and Prior Knowledge driven Features with Probabilistic Ensemble Enhancement},
  booktitle = {Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {52--59},
  abstract  = {This paper describes our system at NLPTEA-2018 Task \#1: Chinese Grammatical Error Diagnosis. Grammatical Error Diagnosis is one of the most challenging NLP tasks，which is to locate grammar errors and tell error types. Our system is built on the model of bidirectional Long Short-Term Memory with a conditional random field layer (BiLSTM-CRF) but integrates with several new features. First, richer features are considered in the BiLSTM-CRF model; second, a probabilistic ensemble approach is adopted; third, Template Matcher are used during a post-processing to bring in human knowledge. In official evaluation, our system obtains the highest F1 scores at identifying error types and locating error positions, the second highest F1 score at sentence level error detection. We also recommend error corrections for specific error types and achieve the best F1 performance among all participants.},
  url       = {http://www.aclweb.org/anthology/W18-3707}
}