  This paper describes the system developed by the USTC-NELSLIP team for
SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER
II). A method named Statistical Construction and Dual Adaptation of Gazetteer
(SCDAG) is proposed for Multilingual Complex NER. The method first utilizes a
statistics-based approach to construct a gazetteer. Secondly, the
representations of gazetteer networks and language models are adapted by
minimizing the KL divergence between them at both the sentence-level and
entity-level. Finally, these two networks are then integrated for supervised
named entity recognition (NER) training. The proposed method is applied to
XLM-R with a gazetteer built from Wikidata, and shows great generalization
ability across different tracks. Experimental results and detailed analysis
verify the effectiveness of the proposed method. The official results show that
our system ranked 1st on one track (Hindi) in this task.
