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arXiv · 2307.16265

Recent Advances in Hierarchical Multi-label Text Classification: A Survey

Rundong Liu·Wenhan Liang·Weijun Luo·Yuxiang Song·He Zhang·Ruohua Xu·Yunfeng Li·Ming Liu
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Abstract

Hierarchical multi-label text classification aims to classify the input text into multiple labels, among which the labels are structured and hierarchical. It is a vital task in many real world applications, e.g. scientific literature archiving. In this paper, we survey the recent progress of hierarchical multi-label text classification, including the open sourced data sets, the main methods, evaluation metrics, learning strategies and the current challenges. A few future research directions are also listed for community to further improve this field.

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Rundong Liu, Wenhan Liang, Weijun Luo, Yuxiang Song, He Zhang, Ruohua Xu, Yunfeng Li, Ming Liu. 2023-07-30. Recent Advances in Hierarchical Multi-label Text Classification: A Survey. https://arxiv.org/abs/2307.16265

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