Abstract
This paper describes our system, which placed third in the Multilingual Track (subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual Complex Named Entity Recognition. Our system’s key contributions are as follows: 1) For multilingual NER tasks, we offered a unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource mixed-code NER task, one can easily enhanced his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we proposed a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, in the test phase, our system received macro-f1 scores of 77.66, 84.35, and 74 on task 12, task 13, and task 9.- Anthology ID:
- 2022.semeval-1.228
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1654–1664
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.228
- DOI:
- 10.18653/v1/2022.semeval-1.228
- Cite (ACL):
- Weichao Gan, Yuanping Lin, Guangbo Yu, Guimin Chen, and Qian Ye. 2022. Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1654–1664, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task (Gan et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.semeval-1.228.pdf
- Data
- MultiCoNER