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.- Anthology ID:
- 2022.semeval-1.220
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1597–1602
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.220
- DOI:
- 10.18653/v1/2022.semeval-1.220
- Cite (ACL):
- Lung-Hao Lee, Chien-Huan Lu, and Tzu-Mi Lin. 2022. NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1597–1602, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model (Lee et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.semeval-1.220.pdf
- Data
- MultiCoNER