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Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification

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Published in:Empirical Software Engineering (Open Access Articles)
Format: Online Article RSS Article
Published: 2026
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container_title Empirical Software Engineering (Open Access Articles)
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journal_source_facet Empirical Software Engineering (Open Access Articles)
publishDate 2026
publishDateSort 2026
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spellingShingle Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
Computer Software-Systems
General
Computer Software-Systems
sub_discipline_display General
sub_discipline_facet General
subject_display Computer Software-Systems
General
Computer Software-Systems
Computer Software-Systems
General
Computer Software-Systems
subject_facet Computer Software-Systems
General
Computer Software-Systems
title Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_auth Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_full Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_fullStr Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_full_unstemmed Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_short Machine learning, deep learning, or large language models: An empirical study on multi-label requirements classification
title_sort machine learning, deep learning, or large language models: an empirical study on multi-label requirements classification
topic Computer Software-Systems
General
Computer Software-Systems
url https://link.springer.com/article/10.1007/s10664-026-10879-3