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This paper describes the system developed by the USTC-NELSLIP team forSemEval-2022 Task 11 Multilingual Complex Named Entity Recognition(MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) toimprove the performance of language models for recognizing complex namedentities. The method first adapts the representations of gazetteer networks tothose of language models by minimizing the KL divergence between them. Afteradaptation, these two networks are then integrated for backend supervised namedentity recognition (NER) training. The proposed method is applied to severalstate-of-the-art Transformer-based NER models with a gazetteer built fromWikidata, and shows great generalization ability across them. The finalpredictions are derived from an ensemble of these trained models. Experimentalresults and detailed analysis verify the effectiveness of the proposed method.The official results show that our system ranked 1st on three tracks (Chinese,Code-mixed and Bangla) and 2nd on the other ten tracks in this task.

USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition