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


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.220.pdf
Data
MultiCoNER