Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification
Multi-label text classification involves assigning multiple relevant categories to a single text, enabling applications in academic indexing, medical diagnostics, and e-commerce. However, existing models often fail to capture complex text-label relationships and lack robust mechanisms for ranking la...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11129041/ |
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| author | Lama Ayash Abdulmohsen Algarni Omar Alqahtani |
| author_facet | Lama Ayash Abdulmohsen Algarni Omar Alqahtani |
| author_sort | Lama Ayash |
| collection | DOAJ |
| description | Multi-label text classification involves assigning multiple relevant categories to a single text, enabling applications in academic indexing, medical diagnostics, and e-commerce. However, existing models often fail to capture complex text-label relationships and lack robust mechanisms for ranking label relevance, limiting their effectiveness. This paper proposes the Label-Aware Hierarchical Ranking Model, a novel approach that combines contextual embeddings, custom attention mechanisms, and gradient boosting to enhance label ranking. The proposed model integrates text and label embeddings with a contextual similarity attention module to capture text-label relationships and inter-label dependencies, followed by a hierarchical ranking mechanism for refining label prioritization. Experiments conducted on both the ArXiv Academic Paper dataset and the Reuters-21578 dataset demonstrate that the proposed model surpasses state-of-the-art models, achieving Precision@1 scores of 86.90 % and 94.47 %, respectively. These results advance multi-label text classification methodologies, offering more accurate, context-aware, and practically applicable classifications. |
| format | Article |
| id | doaj-art-a5b2a50f65d240e29a2d69f2a6ab70f6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a5b2a50f65d240e29a2d69f2a6ab70f62025-08-25T23:12:31ZengIEEEIEEE Access2169-35362025-01-011314592514593710.1109/ACCESS.2025.359994411129041Label-Aware Hierarchical Ranking Model for Multi-Label Text ClassificationLama Ayash0https://orcid.org/0000-0002-6268-9072Abdulmohsen Algarni1https://orcid.org/0000-0002-7556-958XOmar Alqahtani2https://orcid.org/0009-0001-6652-3512Department of Computer Science, King Khalid University, Al Faraa, Abha, Aseer, Saudi ArabiaDepartment of Computer Science, King Khalid University, Al Faraa, Abha, Aseer, Saudi ArabiaDepartment of Computer Science, King Khalid University, Al Faraa, Abha, Aseer, Saudi ArabiaMulti-label text classification involves assigning multiple relevant categories to a single text, enabling applications in academic indexing, medical diagnostics, and e-commerce. However, existing models often fail to capture complex text-label relationships and lack robust mechanisms for ranking label relevance, limiting their effectiveness. This paper proposes the Label-Aware Hierarchical Ranking Model, a novel approach that combines contextual embeddings, custom attention mechanisms, and gradient boosting to enhance label ranking. The proposed model integrates text and label embeddings with a contextual similarity attention module to capture text-label relationships and inter-label dependencies, followed by a hierarchical ranking mechanism for refining label prioritization. Experiments conducted on both the ArXiv Academic Paper dataset and the Reuters-21578 dataset demonstrate that the proposed model surpasses state-of-the-art models, achieving Precision@1 scores of 86.90 % and 94.47 %, respectively. These results advance multi-label text classification methodologies, offering more accurate, context-aware, and practically applicable classifications.https://ieeexplore.ieee.org/document/11129041/Multi-label classificationnatural language processingdeep learningneural networkstext classificationattention mechanisms |
| spellingShingle | Lama Ayash Abdulmohsen Algarni Omar Alqahtani Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification IEEE Access Multi-label classification natural language processing deep learning neural networks text classification attention mechanisms |
| title | Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification |
| title_full | Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification |
| title_fullStr | Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification |
| title_full_unstemmed | Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification |
| title_short | Label-Aware Hierarchical Ranking Model for Multi-Label Text Classification |
| title_sort | label aware hierarchical ranking model for multi label text classification |
| topic | Multi-label classification natural language processing deep learning neural networks text classification attention mechanisms |
| url | https://ieeexplore.ieee.org/document/11129041/ |
| work_keys_str_mv | AT lamaayash labelawarehierarchicalrankingmodelformultilabeltextclassification AT abdulmohsenalgarni labelawarehierarchicalrankingmodelformultilabeltextclassification AT omaralqahtani labelawarehierarchicalrankingmodelformultilabeltextclassification |