Hierarchical Text Classification As Sub-Hierarchy Sequence Generation

• URL: http://arxiv.org/abs/2111.11104v3
• Date: Tue, 7 Nov 2023 01:52:17 GMT
• Title: Hierarchical Text Classification As Sub-Hierarchy Sequence Generation
• Authors: SangHun Im, Gibaeg Kim, Heung-Seon Oh, Seongung Jo, Donghwan Kim
• Abstract summary: Hierarchical text classification (HTC) is essential for various real applications. Recent HTC models have attempted to incorporate hierarchy information into a model structure. We formulate HTC as a sub-hierarchy sequence generation to incorporate hierarchy information into a target label sequence. HiDEC achieved state-of-the-art performance with significantly fewer model parameters than existing models on benchmark datasets.
• Score: 8.062201442038957
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hierarchical text classification (HTC) is essential for various real applications. However, HTC models are challenging to develop because they often require processing a large volume of documents and labels with hierarchical taxonomy. Recent HTC models based on deep learning have attempted to incorporate hierarchy information into a model structure. Consequently, these models are challenging to implement when the model parameters increase for a large-scale hierarchy because the model structure depends on the hierarchy size. To solve this problem, we formulate HTC as a sub-hierarchy sequence generation to incorporate hierarchy information into a target label sequence instead of the model structure. Subsequently, we propose the Hierarchy DECoder (HiDEC), which decodes a text sequence into a sub-hierarchy sequence using recursive hierarchy decoding, classifying all parents at the same level into children at once. In addition, HiDEC is trained to use hierarchical path information from a root to each leaf in a sub-hierarchy composed of the labels of a target document via an attention mechanism and hierarchy-aware masking. HiDEC achieved state-of-the-art performance with significantly fewer model parameters than existing models on benchmark datasets, such as RCV1-v2, NYT, and EURLEX57K.

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