This talk mainly describes the OPDAI submission to SemEval-2022 Task 11 on Chinese complex NER. First, we explore the performance of model-based approaches and their ensemble, finding that fine-tuning the pre-trained Chinese RoBERTa-wwm model with word semantic representation and contextual gazetteer representation performs best among single models. However, the model-based approach performs poorly on test data because of low-context and unseen-entity cases. Then, we extend our system into two stages: (1) generating entity candidates by using neural model, soft-templates and Wikipedia lexicon. (2) predicting the final entity results within a feature-based rank model. For the evaluation, our best submission achieves an F1 score of 0.7954 and attains the third-best score in the Chinese sub-track.

OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outsideWikipedia knowledge

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W25: SemEval-2022: 15th International Workshop on Semantic Evaluation

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## Welcome to NAACL 2022!
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Ze Chen

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OPDAI at SemEval-2022 Task 11: A hybrid approach for Chinese NER using outsideWikipedia knowledge

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