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Enhancing Hierarchical Text Classification through Knowledge Graph Integration

Ye Liu, Kai Zhang, Zhenya Huang, Kehang Wang, Yanghai Zhang, Qi Liu, Enhong Chen


Abstract
Hierarchical Text Classification (HTC) is an essential and challenging subtask of multi-label text classification with a taxonomic hierarchy. Recent advances in deep learning and pre-trained language models have led to significant breakthroughs in the HTC problem. However, despite their effectiveness, these methods are often restricted by a lack of domain knowledge, which leads them to make mistakes in a variety of situations. Generally, when manually classifying a specific document to the taxonomic hierarchy, experts make inference based on their prior knowledge and experience. For machines to achieve this capability, we propose a novel Knowledge-enabled Hierarchical Text Classification model (K-HTC), which incorporates knowledge graphs into HTC. Specifically, K-HTC innovatively integrates knowledge into both the text representation and hierarchical label learning process, addressing the knowledge limitations of traditional methods. Additionally, a novel knowledge-aware contrastive learning strategy is proposed to further exploit the information inherent in the data. Extensive experiments on two publicly available HTC datasets show the efficacy of our proposed method, and indicate the necessity of incorporating knowledge graphs in HTC tasks.
Anthology ID:
2023.findings-acl.358
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5797–5810
Language:
URL:
https://aclanthology.org/2023.findings-acl.358
DOI:
10.18653/v1/2023.findings-acl.358
Bibkey:
Cite (ACL):
Ye Liu, Kai Zhang, Zhenya Huang, Kehang Wang, Yanghai Zhang, Qi Liu, and Enhong Chen. 2023. Enhancing Hierarchical Text Classification through Knowledge Graph Integration. In Findings of the Association for Computational Linguistics: ACL 2023, pages 5797–5810, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Enhancing Hierarchical Text Classification through Knowledge Graph Integration (Liu et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.358.pdf
Video:
 https://aclanthology.org/2023.findings-acl.358.mp4