提升医学实体链接准确率，兼顾知识库结构与上下文信息。
Neighborhood-Aware Dual Biomedical Entity Linking
- 引入邻域感知检索，融合查询与知识库的本体结构
- 双视角重排序（表面形式与上下文）并融合得分，提升精度
- 在五个基准上达顶尖性能，推理效率高，适合临床文本处理
生物医学实体链接将临床和科学文本中的提及项关联到具有本体结构的规范知识库（KB）中，支持文献级信息抽取和病历记录标准化等下游应用。该任务面临多重挑战：知识库包含大量实体、提及项常存在歧义，且标注规范因语料而异。为此，我们提出PILOT，一种三阶段框架：邻域感知检索、双重重排序与得分融合。检索模块通过重构提及项并聚合实体嵌入，注入查询端与知识库端的本体结构；候选集随后从表面形式与上下文两个互补视角评分，并进行融合。PILOT在五个广泛使用的基准上平均表现达到当前最优水平，且推理效率优异。
原文摘要 · Abstract (English)
Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization. The task has several challenges at once: the KB contains large numbers of entities, mentions are often ambiguous, and gold labels follow annotation conventions specific to each corpus. To address these challenges, we propose PILOT, a three-stage framework made up of neighborhood-aware retrieval, dual reranking, and score fusion. The retriever injects ontological structure from both the query and KB side, by reformulating mentions and pooling entity embeddings. The retrieved pool is then scored from two complementary views, one over surface forms and one over context, and fused together. PILOT achieves the state of the art on average across five widely-used benchmarks and remains efficient at inference.
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