Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations

Linli Xu, Sijie Teng, Ruoyu Zhao, Junliang Guo, Chi Xiao, Deqiang Jiang, Bo Ren


Abstract
Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time. The majority of prior studies either focus on reducing the HMTC task into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss. In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works. Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations. Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure. Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.
Anthology ID:
2021.emnlp-main.190
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2459–2468
Language:
URL:
https://aclanthology.org/2021.emnlp-main.190
DOI:
10.18653/v1/2021.emnlp-main.190
Bibkey:
Cite (ACL):
Linli Xu, Sijie Teng, Ruoyu Zhao, Junliang Guo, Chi Xiao, Deqiang Jiang, and Bo Ren. 2021. Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2459–2468, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (Xu et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.190.pdf
Software:
 2021.emnlp-main.190.Software.zip
Video:
 https://aclanthology.org/2021.emnlp-main.190.mp4
Data
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