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

Rundong Liu, Wenhan Liang, Weijun Luo, Yuxiang Song, He Zhang +3 more
7/30/2023
cs.CLcs.AI

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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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Cite this paper

@article{liu2023recent,
  title  = {Recent Advances in Hierarchical Multi-label Text Classification: A Survey},
  author = {Rundong Liu and Wenhan Liang and Weijun Luo and Yuxiang Song and He Zhang and Ruohua Xu and Yunfeng Li and Ming Liu},
  year   = {2023},
  eprint = {2307.16265},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.16265v1}
}

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