Fine-Grained Mention-Level Analysis of Biomedical Entity Linking Models
Résumé
Biomedical Entity Linking (BEL) is essential for structuring knowledge from biomedical texts, yet global evaluation metrics often obscure systematic model weaknesses. We propose a fine-grained evaluation framework that analyzes performance across interpretable mention-level characteristics, including length, lexical variation, synonymy, homonymy, and training frequency. Using the BELB benchmark, we apply this analysis to neural and rule-based systems. Our results show that performance degradation stems from mention-level difficulty, with consistent drops across characteristics that reflect limited training coverage, and expose model weaknesses beyond aggregate scores in a unified benchmark setting.
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