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.

翻译：本文介绍了我们的系统,该系统在多语言轨道(subtask 11)、代码混合轨道(subtask 12)和中国轨道(subtask 9)中排第三位,在SemEval 2022任务11:多语言多语言复杂实体识别中排第三位。我们的系统的主要贡献如下:1)对于多语言网络任务,我们提供了一个统一框架,可以轻松执行单语言或多语言NER任务,2)对于低资源编码混合NER任务,通过实施若干简单的数据增强方法和3,很容易加强他或她的数据集。我们提出了一个模型,可以捕捉到中国词汇语语、词汇边界和词汇图结构信息。最后,我们的系统在测试阶段在子任务11、12和9上分别取得了77.66、84.35和74.00分的宏观F1分数。