Hierarchical contrastive learning for multi-label text classification
Abstract Multi-label text classification presents a significant challenge within the field of text classification, particularly due to the hierarchical nature of labels, where labels are organized in a tree-like structure that captures parent-child and sibling relationships. This hierarchy reflects...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97597-w |
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| author | Wei Zhang Yun Jiang Yun Fang Shuai Pan |
| author_facet | Wei Zhang Yun Jiang Yun Fang Shuai Pan |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | Abstract Multi-label text classification presents a significant challenge within the field of text classification, particularly due to the hierarchical nature of labels, where labels are organized in a tree-like structure that captures parent-child and sibling relationships. This hierarchy reflects semantic dependencies among labels, with higher-level labels representing broader categories and lower-level labels capturing more specific distinctions. Traditional methods often fail to deeply understand and leverage this hierarchical structure, overlooking the subtle semantic differences and correlations that distinguish one label from another. To address this shortcoming, we introduce a novel method called Hierarchical Contrastive Learning for Multi-label Text Classification (HCL-MTC). Our approach leverages the contrastive knowledge embedded within label relationships by constructing a graph representation that explicitly models the hierarchical dependencies among labels. Specifically, we recast multi-label text classification as a multi-task learning problem, incorporating a hierarchical contrastive loss that is computed through a carefully designed sampling process. This unique loss function enables our model to effectively capture both the correlations and distinctions among labels, thereby enhancing the model’s ability to learn the intricacies of the label hierarchy. Experimental results on widely-used datasets, such as RCV1-v2 and WoS, demonstrate that our proposed HCL-MTC model achieves substantial performance gains compared to baseline methods. |
| format | Article |
| id | doaj-art-e5c8c217ef9140369c5d5363bd79c153 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e5c8c217ef9140369c5d5363bd79c1532025-08-20T03:14:03ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-97597-wHierarchical contrastive learning for multi-label text classificationWei Zhang0Yun Jiang1Yun Fang2Shuai Pan3Advanced Institution of Information Technology, Peking UniversityAdvanced Institution of Information Technology, Peking UniversityAdvanced Institution of Information Technology, Peking UniversityAdvanced Institution of Information Technology, Peking UniversityAbstract Multi-label text classification presents a significant challenge within the field of text classification, particularly due to the hierarchical nature of labels, where labels are organized in a tree-like structure that captures parent-child and sibling relationships. This hierarchy reflects semantic dependencies among labels, with higher-level labels representing broader categories and lower-level labels capturing more specific distinctions. Traditional methods often fail to deeply understand and leverage this hierarchical structure, overlooking the subtle semantic differences and correlations that distinguish one label from another. To address this shortcoming, we introduce a novel method called Hierarchical Contrastive Learning for Multi-label Text Classification (HCL-MTC). Our approach leverages the contrastive knowledge embedded within label relationships by constructing a graph representation that explicitly models the hierarchical dependencies among labels. Specifically, we recast multi-label text classification as a multi-task learning problem, incorporating a hierarchical contrastive loss that is computed through a carefully designed sampling process. This unique loss function enables our model to effectively capture both the correlations and distinctions among labels, thereby enhancing the model’s ability to learn the intricacies of the label hierarchy. Experimental results on widely-used datasets, such as RCV1-v2 and WoS, demonstrate that our proposed HCL-MTC model achieves substantial performance gains compared to baseline methods.https://doi.org/10.1038/s41598-025-97597-wContrastive learningHierarchical structureMulti-taskMulti-label text classification |
| spellingShingle | Wei Zhang Yun Jiang Yun Fang Shuai Pan Hierarchical contrastive learning for multi-label text classification Scientific Reports Contrastive learning Hierarchical structure Multi-task Multi-label text classification |
| title | Hierarchical contrastive learning for multi-label text classification |
| title_full | Hierarchical contrastive learning for multi-label text classification |
| title_fullStr | Hierarchical contrastive learning for multi-label text classification |
| title_full_unstemmed | Hierarchical contrastive learning for multi-label text classification |
| title_short | Hierarchical contrastive learning for multi-label text classification |
| title_sort | hierarchical contrastive learning for multi label text classification |
| topic | Contrastive learning Hierarchical structure Multi-task Multi-label text classification |
| url | https://doi.org/10.1038/s41598-025-97597-w |
| work_keys_str_mv | AT weizhang hierarchicalcontrastivelearningformultilabeltextclassification AT yunjiang hierarchicalcontrastivelearningformultilabeltextclassification AT yunfang hierarchicalcontrastivelearningformultilabeltextclassification AT shuaipan hierarchicalcontrastivelearningformultilabeltextclassification |