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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.semeval-1.204.pdf
- Data
- MultiCoNER