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arXiv (Cornell University)
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
December 2024 • Guochao Jiang, Ziqin Luo, Chengwei Hu, Zepeng Ding, Deqing Yang
Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared in the training samples, which hinders the achievement of satisfactory performance. To improve OOE-NER performance, in this paper, we propose a new framework, namely S+NER, which fully leverages sentence-level information. Our S+NER achieves better OOE-NER performance mainly due to the following two particular designs. 1) It first explo…
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