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        <title>Effective Multi-Task Learning for Biomedical Named Entity Recognition</title>
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        <namePart type="given">João</namePart>
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        <namePart type="given">Gonçalo</namePart>
        <namePart type="family">Correia</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Afonso</namePart>
        <namePart type="family">Mendes</namePart>
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            <title>Proceedings of the 24th Workshop on Biomedical Language Processing</title>
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            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Makoto</namePart>
            <namePart type="family">Miwa</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model’s predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.</abstract>
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%0 Conference Proceedings
%T Effective Multi-Task Learning for Biomedical Named Entity Recognition
%A Ruano, João
%A Correia, Gonçalo
%A Barreiros, Leonor
%A Mendes, Afonso
%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 ruano-etal-2025-effective
%X Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model’s predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization.
%R 10.18653/v1/2025.bionlp-1.20
%U https://aclanthology.org/2025.bionlp-1.20/
%U https://doi.org/10.18653/v1/2025.bionlp-1.20
%P 225-239

Markdown (Informal)

[Effective Multi-Task Learning for Biomedical Named Entity Recognition](https://aclanthology.org/2025.bionlp-1.20/) (Ruano et al., BioNLP 2025)

• Effective Multi-Task Learning for Biomedical Named Entity Recognition (Ruano et al., BioNLP 2025)

ACL

• João Ruano, Gonçalo Correia, Leonor Barreiros, and Afonso Mendes. 2025. Effective Multi-Task Learning for Biomedical Named Entity Recognition. In Proceedings of the 24th Workshop on Biomedical Language Processing, pages 225–239, Viena, Austria. Association for Computational Linguistics.