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        <title>Jim at SemEval-2025 Task 5: Multilingual BERT Ensemble</title>
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            <title>Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)</title>
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        <name type="personal">
            <namePart type="given">Sara</namePart>
            <namePart type="family">Rosenthal</namePart>
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        <name type="personal">
            <namePart type="given">Aiala</namePart>
            <namePart type="family">Rosá</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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        <name type="personal">
            <namePart type="given">Marcos</namePart>
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    <abstract>The SemEval-2025 Task 5 calls for the utilization of LLM capabilities to apply controlled subject labels to record descriptions in the multilingual library collection of the German National Library of Science and Technology. The multilingual BERT ensemble system described herein produces subject labels for various record types, including articles, books, conference papers, reports, and theses. Results indicate that for English language article records, bidirectional encoder-only LLMs can achieve high recall in automated subject assignment.</abstract>
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        <url>https://aclanthology.org/2025.semeval-1.313/</url>
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%0 Conference Proceedings
%T Jim at SemEval-2025 Task 5: Multilingual BERT Ensemble
%A Hahn, Jim
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F hahn-2025-jim
%X The SemEval-2025 Task 5 calls for the utilization of LLM capabilities to apply controlled subject labels to record descriptions in the multilingual library collection of the German National Library of Science and Technology. The multilingual BERT ensemble system described herein produces subject labels for various record types, including articles, books, conference papers, reports, and theses. Results indicate that for English language article records, bidirectional encoder-only LLMs can achieve high recall in automated subject assignment.
%U https://aclanthology.org/2025.semeval-1.313/
%P 2407-2412

Markdown (Informal)

[Jim at SemEval-2025 Task 5: Multilingual BERT Ensemble](https://aclanthology.org/2025.semeval-1.313/) (Hahn, SemEval 2025)

• Jim at SemEval-2025 Task 5: Multilingual BERT Ensemble (Hahn, SemEval 2025)

ACL

• Jim Hahn. 2025. Jim at SemEval-2025 Task 5: Multilingual BERT Ensemble. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2407–2412, Vienna, Austria. Association for Computational Linguistics.