Scholarlink

<p>Named entity recognition (NER) underpins information extraction, yet its performance often degrades in specialized domains where annotations are scarce and entity inventories change frequently. Few-shot NER targets such settings, but existing two-stage pipelines that separate span detection from type classification remain vulnerable: domain shift blurs span boundaries, and type representations estimated from tiny support sets are unstable. To address both issues, we propose a semantically enhanced two-stage framework. We first train a boundary-aware span detector with a contrastive objective on a high-resource source domain; then, for each target-domain episode, we construct label-guided hybrid prototypes that fuse label-text semantics with support-set mentions. Results on cross-domain benchmarks, Few-NERD, and a real-world power equipment defect dataset show that our framework consistently outperforms strong two-stage baselines, yielding about 1–3 F1 gains in the most challenging low-resource scenarios.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A semantically enhanced two-stage framework for few-shot named entity recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

查看全文
  • Jingguo Ren,
  • Zhuangzhuang Li,
  • Yi Yang,
  • Runtao Wang

摘要

Named entity recognition (NER) underpins information extraction, yet its performance often degrades in specialized domains where annotations are scarce and entity inventories change frequently. Few-shot NER targets such settings, but existing two-stage pipelines that separate span detection from type classification remain vulnerable: domain shift blurs span boundaries, and type representations estimated from tiny support sets are unstable. To address both issues, we propose a semantically enhanced two-stage framework. We first train a boundary-aware span detector with a contrastive objective on a high-resource source domain; then, for each target-domain episode, we construct label-guided hybrid prototypes that fuse label-text semantics with support-set mentions. Results on cross-domain benchmarks, Few-NERD, and a real-world power equipment defect dataset show that our framework consistently outperforms strong two-stage baselines, yielding about 1–3 F1 gains in the most challenging low-resource scenarios.