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The MultiCoNER II task aims to detect complex, ambiguous, and fine-grainednamed entities in low-context situations and noisy scenarios like the presenceof spelling mistakes and typos for multiple languages. The task posessignificant challenges due to the scarcity of contextual information, the highgranularity of the entities(up to 33 classes), and the interference of noisydata. To address these issues, our team {\bf PAI} proposes a universal NamedEntity Recognition (NER) system that integrates external entity information toimprove performance. Specifically, our system retrieves entities withproperties from the knowledge base (i.e. Wikipedia) for a given text, thenconcatenates entity information with the input sentence and feeds it intoTransformer-based models. Finally, our system wins 2 first places, 4 secondplaces, and 1 third place out of 13 tracks. The code is publicly available at\url{https://github.com/diqiuzhuanzhuan/semeval-2023}.

PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information