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

• URL: http://arxiv.org/abs/2204.07459v1
• Date: Thu, 14 Apr 2022 07:51:36 GMT
• Title: Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task
• Authors: Weichao Gan, Yuanping Lin, Guangbo Yu, Guimin Chen and Qian Ye
• Abstract summary: 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) Our system's key contributions are as follows: 1) For multilingual NER tasks, we offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods, and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border
• Score: 10.167123492952694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 offer an unified framework with which one can easily execute single-language or multilingual NER tasks, 2) for low-resource code-mixed NER task, one can easily enhance his or her dataset through implementing several simple data augmentation methods and 3) for Chinese tasks, we propose a model that can capture Chinese lexical semantic, lexical border, and lexical graph structural information. Finally, our system achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9, respectively, during the testing phase.

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