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
https://github.com/diqiuzhuanzhuan/semeval-2023.

本文介绍了一个通用的命名实体识别系统，其利用来自于 Wikipedia 的知识库信息，将其与输入文本进行拼接并使用 Transformer 模型进行训练，用于解决多语言、低上下文、存在拼写错误或者错别字的情况下进行的 MultiCoNER II 任务，取得了 13 个赛道中 2 个第一名、4 个第二名、1 个第三名的成绩。