论文标题
Semeval-2022任务11：多语言NER任务的QTRADE AI：一个统一的框架
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task
论文作者
论文摘要
本文描述了我们的系统，该系统在多语言轨道（子任务11）中排名第三，在代码混合轨道（子任务12）中排名第四，在Semeval 2022任务11中的中国轨道（子任务9）中排名第七，在中国轨道（子任务9）中排名第七：多语音多语言多语言综合体命名实体识别。 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词汇图结构信息。最后，在测试阶段，我们的系统在子任务11、12和9上分别达到了77.66、84.35和74.00的宏F1分数。
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.
