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

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arxiv 2307.16265 v1 pith:SCPZIE4O submitted 2023-07-30 cs.CL cs.AI

Rundong Liu , Wenhan Liang , Weijun Luo , Yuxiang Song , He Zhang , Ruohua Xu , Yunfeng Li , Ming Liu This is my paper · ORCID
classification cs.CLcs.AI
keywords hierarchicaltextclassificationmulti-labellabelsrecentsurveyadvances
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification

    cs.CL 2024-12 conditional novelty 6.0 of 10

    A cross-domain benchmark shows that in hierarchical text classification, methods from other domains often beat domain-specific state of the art, and dataset traits matter more than domain origin.

  2. Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?

    cs.AI 2024-12 reject novelty 5.0 of 10

    A retrieval plus zero-shot LLM pipeline is reported to give 94.3% SME-approval accuracy on SSRN hierarchical multi-label classification, versus 61.5% for fine-tuned SPECTER2, with no retraining.

  3. A Platform for Investigating Public Health Content with Efficient Concern Classification

    cs.CL 2025-06 conditional novelty 4.0 of 10

    The paper introduces ConcernScope, a teacher-student platform where GPT-4 labels training data and a BERT model classifies texts into VaxConcerns categories, with a pilot trend analysis on 186,000 passages.

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