BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking
BioELX is a novel two-stage framework for cross-lingual biomedical entity linking that requires no annotated training data. It enhances SapBERT with multilingual aliases from Wikidata and uses a pre-trained LLM for context-aware disambiguation. Experiments on five benchmarks show significant improvements, especially for low-resource languages like Turkish, Korean, and Thai.
Article intelligence
Key points
- Proposes BioELX, a zero-shot cross-lingual BEL framework using alias-based retrieval and LLM ranking.
- In Stage 1, enriches SapBERT with multilingual aliases from Wikidata for better candidate retrieval.
- In Stage 2, uses a pre-trained LLM ranker for context-aware disambiguation without supervised training.
- Achieves state-of-the-art performance on five benchmarks, with large gains for low-resource languages.
Why it matters
This matters because proposes BioELX, a zero-shot cross-lingual BEL framework using alias-based retrieval and LLM ranking.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27380] BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking
[Submitted on 9 Apr 2026]
Title:BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking
View a PDF of the paper titled BioELX: Cross-lingual Biomedical Entity Linking via Alias-based Retrieval and LLM Ranking, by Yi Wang and 3 other authors
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Abstract:Cross-lingual biomedical entity linking (BEL) maps mentions in any language to unique identifiers in a biomedical knowledge base (KB), supporting clinical and biomedical NLP applications. However, expert-annotated training data for BEL are costly, especially for low-resource languages. Moreover, many cross-lingual BEL systems rely on SapBERT-based retrievers trained on predominantly English aliases in the KB, leading to poor generalization to unseen non-English mentions and limited context-aware disambiguation. We propose BioELX, a two-stage cross-lingual BEL framework that requires no task-specific annotated training corpora. In Stage~1, we enrich SapBERT training with Wikidata-derived multilingual aliases and use the resulting retriever to improve cross-lingual candidate retrieval. In Stage~2, we perform context-aware disambiguation with a pre-trained LLM ranker that jointly considers the mention context and candidate, eliminating the need for supervised training. Experiments on five benchmarks (XL-BEL, EMEA, Patent, WikiMed-DE, and MedMentions) show that BioELX achieves new state-of-the-art performance. It improves average Recall@1 on XL-BEL by +19.2, with especially large gains for low-resource languages, e.g., +21.6 on Turkish, +22.1 on Korean, +30.8 on Thai, and delivers consistent improvements on EMEA (+6.2), Patent (+5.4), and WikiMed-DE (+12.8). Code and resources will be released upon publication.
Comments: 12 pages, 3 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27380 [cs.CL]
(or arXiv:2605.27380v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.27380
arXiv-issued DOI via DataCite
Submission history
From: Yi Wang [view email] [v1] Thu, 9 Apr 2026 20:07:20 UTC (347 KB)
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