arXiv:2605.27380v1 Announce Type: cross 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 cr
The proliferation of LLMs and the increasing demand for advanced NLP in specialized domains like biomedicine are driving innovation in cross-lingual entity linking, especially for low-resource languages.
This development addresses a critical barrier in global biomedical research and clinical applications by enabling more accurate and accessible knowledge extraction from diverse linguistic sources.
Biomedical entity linking becomes more effective and less reliant on expert-annotated English data, expanding its utility across a wider range of languages and medical communities.
- · Biomedical researchers
- · Global healthcare providers
- · LLM developers
- · NLP practitioners
- · Monolingual biomedical data processing systems
- · Services reliant solely on English biomedical data
Improved accuracy and efficiency in cross-lingual biomedical information retrieval and knowledge base population.
Accelerated drug discovery, disease diagnosis, and treatment development through better access to global medical literature.
Enhanced collaboration and knowledge sharing among international biomedical communities, potentially leading to more equitable healthcare outcomes worldwide.
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