NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model
Lung‐Hao Lee, Chien-Huan Lu, Tzu-Mi Lin · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
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.