@inproceedings{lee-etal-2022-ncuee,
    title = "{NCUEE}-{NLP} at {S}em{E}val-2022 Task 11: {C}hinese Named Entity Recognition Using the {BERT}-{B}i{LSTM}-{CRF} Model",
    author = "Lee, Lung-Hao  and
      Lu, Chien-Huan  and
      Lin, Tzu-Mi",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.semeval-1.220/",
    doi = "10.18653/v1/2022.semeval-1.220",
    pages = "1597--1602",
    abstract = "This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our best submission, with a macro-averaging F1-score of 0.7418, ranked the seventh position out of 21 teams."
}