从普通标注数据中学习嵌套实体识别，让模型自动发现层级关系。
Learning Nested Named Entity Recognition from Flat Annotations
- 用字符串包含、伪嵌套数据等方法从平铺标注中挖掘嵌套结构
- 在俄语数据集上达到26.37%的内部实体F1，接近全标注监督效果的60%
- 适合资源有限但需处理嵌套实体的中文/多语言命名实体场景
嵌套命名实体识别需识别包含于其他实体中的实体，但标注成本高。尽管存在大量平铺式命名实体标注数据，嵌套标注资源仍稀缺。本文研究模型能否仅从平铺标注中学习嵌套结构，评估了四种方法：字符串包含（子串匹配）、实体破坏（生成伪嵌套数据）、平铺中性化（降低误报信号）以及微调+大模型混合管道。在包含29种实体类型的俄语基准数据集NEREL上，21%的实体为嵌套结构，最佳组合方法取得26.37%的内层实体F1，缩小了与完整嵌套标注监督下性能差距的40%。代码已开源。
原文摘要 · Abstract (English)
Nested named entity recognition identifies entities contained within other entities, but requires expensive multi-level annotation. While flat NER corpora exist abundantly, nested resources remain scarce. We investigate whether models can learn nested structure from flat annotations alone, evaluating four approaches: string inclusions (substring matching), entity corruption (pseudo-nested data), flat neutralization (reducing false negative signal), and a hybrid fine-tuned + LLM pipeline. On NEREL, a Russian benchmark with 29 entity types where 21% of entities are nested, our best combined method achieves 26.37% inner F1, closing 40% of the gap to full nested supervision. Code is available at https://github.com/fulstock/Learning-from-Flat-Annotations.
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