Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task

Weichao Gan, Yuanping Lin, Guangbo Yu, Guimin Chen, Qian Ye


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
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:
1654–1664
Language:
URL:
https://aclanthology.org/2022.semeval-1.228
DOI:
10.18653/v1/2022.semeval-1.228
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.228.pdf
Video:
 https://aclanthology.org/2022.semeval-1.228.mp4
Data
MultiCoNER