感知邻域的双生物医学实体链接
Neighborhood-Aware Dual Biomedical Entity Linking
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- University of Michigan（密歇根大学）
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中文总结 AI 辅助
针对生物医学实体链接的挑战，提出三阶段框架PILOT，在五个基准上取得最优性能且推理高效。
中文摘要 AI 辅助
生物医学实体链接将临床和科学文本中的提及项与具有本体结构的精选知识库（KB）中的实体关联，可支持文献规模信息抽取、患者记录标准化等下游应用。该任务同时面临多项挑战：知识库包含大量实体，提及项常存在歧义，且黄金标签遵循各语料库特有的标注规范。为应对这些挑战，本文提出PILOT，一个由感知邻域的检索、双重排序和分数融合构成的三阶段框架。该检索器通过重构提及项并池化实体嵌入，同时注入查询侧与知识库侧的本体结构；随后从表面形式和上下文这两个互补视角对检索池打分并融合分数。PILOT在五个广泛使用的基准上平均达到了最优性能，且推理时保持高效。
英文摘要
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.
