  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.
