PAI at SemEval-2023 Task 2: A Universal System for Named Entity
  Recognition with External Entity Information
Long Ma Kai Lu Tianbo Che Hailong Huang Weiguo Gao Xuan Li
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}.