BioELX: 基于别名的检索与LLM排序的跨语言生物医学实体链接
BioELX: Context-Aware Cross-lingual Biomedical Entity Linking without Task-Specific Supervision
AI总结：
提出BioELX两阶段框架，通过维基数据多语言别名增强SapBERT检索器，并利用预训练LLM排序器进行上下文感知消歧，无需标注数据即在多个基准上取得最佳性能。
摘要：
跨语言生物医学实体链接（BEL）将任何语言的提及映射到生物医学知识库（KB）中的唯一标识符，支持临床和生物医学NLP应用。然而，BEL的专家标注训练数据成本高昂，尤其是对于低资源语言。此外，许多跨语言BEL系统依赖于基于SapBERT的检索器，这些检索器主要在KB中的英语别名上训练，导致对未见过的非英语提及泛化能力差，且上下文感知消歧有限。我们提出BioELX，一个两阶段跨语言BEL框架，无需任务特定的标注训练语料。在第一阶段，我们用维基数据派生的多语言别名丰富SapBERT训练，并使用得到的检索器改进跨语言候选检索。在第二阶段，我们使用预训练LLM排序器进行上下文感知消歧，该排序器联合考虑提及上下文和候选，消除了监督训练的需要。在五个基准（XL-BEL、EMEA、Patent、WikiMed-DE和MedMentions）上的实验表明，BioELX实现了新的最先进性能。它在XL-BEL上将平均Recall@1提高了+19.2，尤其是低资源语言提升显著，例如土耳其语+21.6、韩语+22.1、泰语+30.8，并在EMEA（+6.2）、Patent（+5.4）和WikiMed-DE（+12.8）上持续改进。代码和资源将在发表后发布。
英文摘要：
Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base, supporting clinical and biomedical NLP applications. We identify two issues affecting current systems. First, the UMLS (Bodenreider,2004) aliases used to train cross-lingual BEL retrievers are heavily skewed toward English, so retrievers generalize poorly to non-English mentions. Second, although context is often necessary for disambiguation, naively injecting context into retrievers trained only to align aliases severely degrades retrieval. We propose BioELX, a retrieve-rerank framework that addresses both issues. For retrieval, we continue training SapBERT_multi (Liu et al., 2021b) using Wikidata-derived cross-lingual alias supervision, forming shared concept neighborhoods across languages. For reranking, we adapt pretrained LLM rerankers to entity linking through mention-anchored prompting, which marks the target mention so that rerankers score candidates with respect to the intended mention rather than other salient tokens in the context. Experiments show that BioELX achieves new state-of-the-art results on four cross-lingual BEL benchmarks, improving Recall@1 by 4.8 to 18.2 percentage points over prior best results, without any task-specific BEL annotations. Our code and resources are available at https://github.com/AI4MedCode/BioELX.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
