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GACaps-HTC: graph attention capsule network for hierarchical text classification

Applied Intelligence volume 53, pages 20577–20594 (2023)Cite this article

Abstract

Hierarchical text classification has been receiving increasing attention due to its vast range of applications in real-world natural language processing tasks. While previous approaches have focused on effectively exploiting the label hierarchy for classification or capturing latent label relationships, few studies have integrated these concepts. In this work, we propose a graph attention capsule network for hierarchical text classification (GACaps-HTC), designed to capture both the explicit hierarchy and implicit relationships of labels. A graph attention network is employed to incorporate the information on the label hierarchy into a textual representation, whereas a capsule network infers classification probabilities by understanding the latent label relationships via iterative updates. The proposed approach is optimized using a loss term designed to address the innate label imbalance issue of the task. Experiments were conducted on two widely used text classification datasets, the WOS-46985 dataset and the RCV1 dataset. The results reveal that the proposed approach achieved a 0.6% gain and a 2.0% gain in micro-F1 and macro-F1 scores, respectively, on the WOS-46985 dataset and a 0.3% gain and a 2.2% gain in micro-F1 and macro-F1 scores, respectively, on the RCV1 dataset compared to the previous state-of-the-art approaches. Further ablation studies show that each component in GACaps-HTC played a part in enhancing the classification performance.

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Data Availability

The datasets analysed using the current study are available in the Mendeley Data repository (https://data.mendeley.com/datasets/9rw3vkcfy4/6) and the Text Retrieval Conference repository (https://trec.nist.gov/data/reuters/reuters.html) as described in the manuscript.

Code Availability

The code for reproducing the results provided in the manuscript will be made public upon acceptance.

Notes

  1. https://www.webofscience.com/

  2. https://data.mendeley.com/datasets/9rw3vkcfy4/6

  3. https://www.reuters.com/

  4. https://trec.nist.gov/data/reuters/reuters.html

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1053366). The Institute of Engineering Research at Seoul National University provided research facilities for this work.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1053366). The Institute of Engineering Research at Seoul National University provided research facilities for this work.

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Authors and Affiliations

  1. Department of Industrial Engineering & Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea

    Jinhyun Bang & Jonghun Park

  2. Department of AI, Big Data & Management, Kookmin University, 77 Jungnung-ro, Seongbuk-gu, Seoul, 02707, South Korea

    Jonghyuk Park

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All authors contributed to the conceptualization. Methodology design, analysis, and visualization were performed by Jinhyun Bang. Funding was acquired by Jonghun Park. This study was supervised by Jonghun Park and Jonghyuk Park. Original draft of the manuscript was written by Jinhyun Bang and reviewed, edited, and approved by all authors.

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Bang, J., Park, J. & Park, J. GACaps-HTC: graph attention capsule network for hierarchical text classification. Appl Intell 53, 20577–20594 (2023). https://doi.org/10.1007/s10489-023-04585-6

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