Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916563858227200 |
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| author | Jiang, Guochao Luo, Ziqin Hu, Chengwei Ding, Zepeng Yang, Deqing |
| author_facet | Jiang, Guochao Luo, Ziqin Hu, Chengwei Ding, Zepeng Yang, Deqing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_08434 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy Jiang, Guochao Luo, Ziqin Hu, Chengwei Ding, Zepeng Yang, Deqing Computation and Language Artificial Intelligence 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. |
| title | Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08434 |