构建多领域医学实体链接数据集，支持可解释模型研发。
MedPath: Multi-Domain Cross-Vocabulary Hierarchical Paths for Biomedical Entity Linking
- 基于9个标注数据集构建跨词汇层级路径
- 覆盖62个生物医学术语库，含11个体系的完整路径
- 助力临床NLP模型实现可解释性与互操作性
生物医学命名实体识别与实体链接进展受限于数据碎片化、可解释模型资源不足以及语义盲视评估指标。为此，我们提出MedPath，一个大规模多领域生物医学实体链接数据集，基于九个已有的专家标注数据集构建。在MedPath中，所有实体均：1）使用最新版统一医学语言系统（UMLS）进行标准化；2）扩展映射至62个其他生物医学词汇表；3）关键地，包含最多11个生物医学词汇表中的完整本体路径（从一般到具体）。MedPath直接推动生物医学自然语言处理新研究前沿，支持语义丰富且可解释的实体链接系统训练与评估，促进下一代可解释、可互操作的临床自然语言处理模型发展。
原文摘要 · Abstract (English)
Progress in biomedical Named Entity Recognition (NER) and Entity Linking (EL) is currently hindered by a fragmented data landscape, a lack of resources for building explainable models, and the limitations of semantically-blind evaluation metrics. To address these challenges, we present MedPath, a large-scale and multi-domain biomedical EL dataset that builds upon nine existing expert-annotated EL datasets. In MedPath, all entities are 1) normalized using the latest version of the Unified Medical Language System (UMLS), 2) augmented with mappings to 62 other biomedical vocabularies and, crucially, 3) enriched with full ontological paths -- i.e., from general to specific -- in up to 11 biomedical vocabularies. MedPath directly enables new research frontiers in biomedical NLP, facilitating training and evaluation of semantic-rich and interpretable EL systems, and the development of the next generation of interoperable and explainable clinical NLP models.
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