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This paper describes the system developed by the USTC-NELSLIP team forSemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNERII). A method named Statistical Construction and Dual Adaptation of Gazetteer(SCDAG) is proposed for Multilingual Complex NER. The method first utilizes astatistics-based approach to construct a gazetteer. Secondly, therepresentations of gazetteer networks and language models are adapted byminimizing the KL divergence between them at both the sentence-level andentity-level. Finally, these two networks are then integrated for supervisednamed entity recognition (NER) training. The proposed method is applied toXLM-R with a gazetteer built from Wikidata, and shows great generalizationability across different tracks. Experimental results and detailed analysisverify the effectiveness of the proposed method. The official results show thatour system ranked 1st on one track (Hindi) in this task.

USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER