@inproceedings{zhang-etal-2018-cmmc,
    title = "{CMMC}-{BDRC} Solution to the {NLP}-{TEA}-2018 {C}hinese Grammatical Error Diagnosis Task",
    author = "Zhang, Yongwei  and
      Hu, Qinan  and
      Liu, Fang  and
      Gu, Yueguo",
    editor = "Tseng, Yuen-Hsien  and
      Chen, Hsin-Hsi  and
      Ng, Vincent  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-3726/",
    doi = "10.18653/v1/W18-3726",
    pages = "180--187",
    abstract = "Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual {\&} Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task."
}