TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text Classification Article Swipe
Fei Zhao
,
Qing Ai
,
Xiangna Li
,
Wenhui Wang
,
Qingyun Gao
,
Yichun Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11460-z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-024-11460-z
Extreme multi-label text classification (XMTC) annotates related labels for unknown text from large-scale label sets. Transformer-based methods have become the dominant approach for solving the XMTC task due to their effective text representation capabilities. However, the existing Transformer-based methods fail to effectively exploit the correlation between labels in the XMTC task. To address this shortcoming, we propose a novel model called TLC-XML, i.e., a Transformer with label correlation for extreme multi-label text classification. TLC-XML comprises three modules: Partition, Matcher and Ranker. In the Partition module, we exploit the semantic and co-occurrence information of labels to construct the label correlation graph, and further partition the strongly correlated labels into the same cluster. In the Matcher module, we propose cluster correlation learning, which uses the graph convolutional network (GCN) to extract the correlation between clusters. We then introduce these valuable correlations into the classifier to match related clusters. In the Ranker module, we propose label interaction learning, which aggregates the raw label prediction with the information of the neighboring labels. The experimental results on benchmark datasets show that TLC-XML significantly outperforms state-of-the-art XMTC methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11063-024-11460-z
- https://link.springer.com/content/pdf/10.1007/s11063-024-11460-z.pdf
- OA Status
- hybrid
- Cited By
- 9
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391723614
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- OpenAlex ID
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https://openalex.org/W4391723614Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11063-024-11460-zDigital Object Identifier
- Title
-
TLC-XML: Transformer with Label Correlation for Extreme Multi-label Text ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-10Full publication date if available
- Authors
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Fei Zhao, Qing Ai, Xiangna Li, Wenhui Wang, Qingyun Gao, Yichun LiuList of authors in order
- Landing page
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https://doi.org/10.1007/s11063-024-11460-zPublisher landing page
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https://link.springer.com/content/pdf/10.1007/s11063-024-11460-z.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11063-024-11460-z.pdfDirect OA link when available
- Concepts
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Computer science, Exploit, XML, Artificial intelligence, Correlation, Pattern recognition (psychology), Classifier (UML), Transformer, Data mining, Machine learning, Mathematics, Quantum mechanics, Physics, Computer security, Geometry, Voltage, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 5, 2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_year | 2024 |
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