Lung-Hao Lee, Chien-Huan Lu, and Tzu-Mi Lin.
In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval’22), 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.
