  The MultiCoNER \RNum{2} shared task aims to tackle multilingual named entity
recognition (NER) in fine-grained and noisy scenarios, and it inherits the
semantic ambiguity and low-context setting of the MultiCoNER \RNum{1} task. To
cope with these problems, the previous top systems in the MultiCoNER \RNum{1}
either incorporate the knowledge bases or gazetteers. However, they still
suffer from insufficient knowledge, limited context length, single retrieval
strategy. In this paper, our team \textbf{DAMO-NLP} proposes a unified
retrieval-augmented system (U-RaNER) for fine-grained multilingual NER. We
perform error analysis on the previous top systems and reveal that their
performance bottleneck lies in insufficient knowledge. Also, we discover that
the limited context length causes the retrieval knowledge to be invisible to
the model. To enhance the retrieval context, we incorporate the entity-centric
Wikidata knowledge base, while utilizing the infusion approach to broaden the
contextual scope of the model. Also, we explore various search strategies and
refine the quality of retrieval knowledge. Our system\footnote{We will release
the dataset, code, and scripts of our system at {\small
\url{https://github.com/modelscope/AdaSeq/tree/master/examples/U-RaNER}}.} wins
9 out of 13 tracks in the MultiCoNER \RNum{2} shared task. Additionally, we
compared our system with ChatGPT, one of the large language models which have
unlocked strong capabilities on many tasks. The results show that there is
still much room for improvement for ChatGPT on the extraction task.
