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        <title>Accelerating Cross-Encoders in Biomedical Entity Linking</title>
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        <namePart type="given">Javier</namePart>
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            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Biomedical entity linking models disambiguate mentions in text by matching them with unique biomedical concepts. This problem is commonly addressed using a two-stage pipeline comprising an inexpensive candidate generator, which filters a subset of suitable entities for a mention, and a costly but precise reranker that provides the final matching between the mention and the concept. With the goal of applying two-stage entity linking at scale, we explore the construction of effective cross-encoder reranker models, capable of scoring multiple mention-entity pairs simultaneously. Through experiments on four entity linking datasets, we show that our cross-encoder models provide between 2.7 to 36.97 times faster training speeds and 3.42 to 26.47 times faster inference speeds than a base cross-encoder model capable of scoring only one entity, while achieving similar accuracy (differences between -3.42% to 2.76% Acc@1).</abstract>
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%0 Conference Proceedings
%T Accelerating Cross-Encoders in Biomedical Entity Linking
%A Sanz-Cruzado, Javier
%A Lever, Jake
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Tsujii, Junichi
%S Proceedings of the 24th Workshop on Biomedical Language Processing
%D 2025
%8 August
%I Association for Computational Linguistics
%C Viena, Austria
%@ 979-8-89176-275-6
%F sanz-cruzado-lever-2025-accelerating
%X Biomedical entity linking models disambiguate mentions in text by matching them with unique biomedical concepts. This problem is commonly addressed using a two-stage pipeline comprising an inexpensive candidate generator, which filters a subset of suitable entities for a mention, and a costly but precise reranker that provides the final matching between the mention and the concept. With the goal of applying two-stage entity linking at scale, we explore the construction of effective cross-encoder reranker models, capable of scoring multiple mention-entity pairs simultaneously. Through experiments on four entity linking datasets, we show that our cross-encoder models provide between 2.7 to 36.97 times faster training speeds and 3.42 to 26.47 times faster inference speeds than a base cross-encoder model capable of scoring only one entity, while achieving similar accuracy (differences between -3.42% to 2.76% Acc@1).
%R 10.18653/v1/2025.bionlp-1.13
%U https://aclanthology.org/2025.bionlp-1.13/
%U https://doi.org/10.18653/v1/2025.bionlp-1.13
%P 136-147

Markdown (Informal)

[Accelerating Cross-Encoders in Biomedical Entity Linking](https://aclanthology.org/2025.bionlp-1.13/) (Sanz-Cruzado & Lever, BioNLP 2025)

• Accelerating Cross-Encoders in Biomedical Entity Linking (Sanz-Cruzado & Lever, BioNLP 2025)

ACL

• Javier Sanz-Cruzado and Jake Lever. 2025. Accelerating Cross-Encoders in Biomedical Entity Linking. In Proceedings of the 24th Workshop on Biomedical Language Processing, pages 136–147, Viena, Austria. Association for Computational Linguistics.