Summary of Mitigating Out-of-entity Errors in Named Entity Recognition: a Sentence-level Strategy, by Guochao Jiang et al.
Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy
by Guochao Jiang, Ziqin Luo, Chengwei Hu, Zepeng Ding, Deqing Yang
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed S+NER framework improves named entity recognition (NER) performance by leveraging sentence-level information. The model first uses a pre-trained language model to understand the target entity’s context with a template set, then refines its representation using contrastive learning and template pooling. This approach outperforms state-of-the-art OOE-NER models on five benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The S+NER framework helps computers better recognize named entities in text by using sentence-level information. It works by first understanding the context of an entity with a pre-trained language model, then refining its representation to get better results. This approach does better than other state-of-the-art models on five different datasets. |
Keywords
» Artificial intelligence » Language model » Named entity recognition » Ner
