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.

本文介绍了我们在 SemEval 2022 Task 11：MultiCoNER Multilingual Complex Named Entity Recognition 中取得的成果，即：在多语言 NER 任务中，提供了一个统一的框架；在低资源代码混合 NER 任务中，采用了几种简单的数据增强方法；在中文任务中，提出了一种可以捕捉中文词汇语义、词汇边界和词汇图结构信息的模型。最后，我们的系统在子任务 11、12 和 9 的测试阶段分别获得了 77.66、84.35 和 74.00 的宏 F1 得分。