Exploiting Global and Local Hierarchies for Hierarchical Text Classification

Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang


Abstract
Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it makes these methods hard to exploit hierarchical information. Contrary to global hierarchy, local hierarchy as a structured labels hierarchy corresponding to each text sample. It is dynamic and relevant to text samples, which is ignored in previous methods. To exploit global and local hierarchies, we propose Hierarchy-guided BERT with Global and Local hierarchies (HBGL), which utilizes the large-scale parameters and prior language knowledge of BERT to model both global and local hierarchies. Moreover, HBGL avoids the intentional fusion of semantic and hierarchical modules by directly modeling semantic and hierarchical information with BERT. Compared with the state-of-the-art method HGCLR, our method achieves significant improvement on three benchmark datasets.
Anthology ID:
2022.emnlp-main.268
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4030–4039
Language:
URL:
https://aclanthology.org/2022.emnlp-main.268
DOI:
10.18653/v1/2022.emnlp-main.268
Bibkey:
Cite (ACL):
Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, and Qinghong Yang. 2022. Exploiting Global and Local Hierarchies for Hierarchical Text Classification. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4030–4039, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Exploiting Global and Local Hierarchies for Hierarchical Text Classification (Jiang et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.268.pdf