The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. The task poses significant challenges due to the scarcity of contextual information, the high granularity of the entities(up to 33 classes), and the interference of noisy data. To address these issues, our team {\bf PAI} proposes a universal Named Entity Recognition (NER) system that integrates external entity information to improve performance. Specifically, our system retrieves entities with properties from the knowledge base (i.e. Wikipedia) for a given text, then concatenates entity information with the input sentence and feeds it into Transformer-based models. Finally, our system wins 2 first places, 4 second places, and 1 third place out of 13 tracks. The code is publicly available at \url{https://github.com/diqiuzhuanzhuan/semeval-2023}.

翻译：MultiCoNER II任务旨在检测低上下文环境及存在拼写错误、打字错误等多语言噪声场景中的复杂、歧义及细粒度命名实体。该任务因上下文信息稀缺、实体粒度高（最多33个类别）以及噪声数据的干扰而面临重大挑战。为解决这些问题，我们的团队{\bf PAI}提出了一种整合外部实体信息以提升性能的通用命名实体识别（NER）系统。具体而言，我们的系统从知识库（如维基百科）中检索给定文本的实体及其属性，然后将实体信息与输入句子拼接后输入基于Transformer的模型。最终，我们的系统在13个赛道中赢得2个第一名、4个第二名和1个第三名。代码已发布于\url{https://github.com/diqiuzhuanzhuan/semeval-2023}。