KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition.

International Workshop on Semantic Evaluation (SemEval )(2022)

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摘要
In this work, we introduce our system to the SemEval 2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER) competition.Our team (KDDIE) attempted the sub-task of Named Entity Recognition (NER) for the language of English in the challenge and reported our results.For this task, we use transfer learning method: fine-tuning the pre-trained language models (PLMs) on the competition dataset.Our two approaches are the BERTbased PLMs and PLMs with additional layer such as Condition Random Field.We report our finding and results in this report.
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关键词
Named Entity Recognition,Multi-label Learning,Multilingual Neural Machine Translation,Neural Machine Translation,Machine Translation
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