OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge

Ze Chen, Kangxu Wang, Jiewen Zheng, Zijian Cai, Jiarong He, Jin Gao


Abstract
This article describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER. First, we explore the performance of model-based approaches and their ensemble, finding that fine-tuning the pre-trained Chinese RoBERTa-wwm model with word semantic representation and contextual gazetteer representation performs best among single models. However, the model-based approach performs poorly on test data because of low-context and unseen-entity cases. Then, we extend our system into two stages: (1) generating entity candidates by using neural model, soft-templates and Wikipedia lexicon. (2) predicting the final entity results within a feature-based rank model. For the evaluation, our best submission achieves an F1 score of 0.7954 and attains the third-best score in the Chinese sub-track.
Anthology ID:
2022.semeval-1.204
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:
1488–1493
Language:
URL:
https://aclanthology.org/2022.semeval-1.204
DOI:
10.18653/v1/2022.semeval-1.204
Bibkey:
Cite (ACL):
Ze Chen, Kangxu Wang, Jiewen Zheng, Zijian Cai, Jiarong He, and Jin Gao. 2022. OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1488–1493, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outside Wikipedia knowledge (Chen et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.204.pdf
Video:
 https://aclanthology.org/2022.semeval-1.204.mp4
Data
MultiCoNER