Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy

• URL: http://arxiv.org/abs/2412.08434v2
• Date: Mon, 13 Jan 2025 14:13:38 GMT
• Title: Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
• Authors: Guochao Jiang, Ziqin Luo, Chengwei Hu, Zepeng Ding, Deqing Yang,
• Abstract summary: We propose a new framework, namely S+NER, which fully leverages sentence-level information. Our experiments on five benchmark datasets have demonstrated that, our S+NER outperforms some state-of-the-art OOE-NER models.
• Score: 7.099196306792118
• License:
• Abstract: 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 exploits the pre-trained language model's capability of understanding the target entity's sentence-level context with a template set. 2) Then, it refines the sentence-level representation based on the positive and negative templates, through a contrastive learning strategy and template pooling method, to obtain better NER results. Our extensive experiments on five benchmark datasets have demonstrated that, our S+NER outperforms some state-of-the-art OOE-NER models.

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