# Weakly-Supervised Hierarchical Text Classification

arXiv: https://arxiv.org/abs/1812.11270 · HTML: https://mlsanity.com/paper/1812.11270/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2018.

## Built on by (most cited first, 30 of 42)

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- [Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification](https://mlsanity.com/paper/2201.03194/) (2022)
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- [CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoning](https://mlsanity.com/paper/2305.04808/) (2023)
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- [Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data](https://mlsanity.com/paper/2109.10856/) (2021)
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- [LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification](https://mlsanity.com/paper/2205.12528/) (2022)
- [Weakly Supervised Text Classification using Supervision Signals from a Language Model](https://mlsanity.com/paper/2205.06604/) (2022)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://mlsanity.com/paper/2211.01981/) (2022)
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- [A Capsule Network for Hierarchical Multi-Label Image Classification](https://mlsanity.com/paper/2209.05723/) (2022)
- [Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers](https://mlsanity.com/paper/2306.14003/) (2023)
- [Self-supervised Topic Taxonomy Discovery in the Box Embedding Space](https://mlsanity.com/paper/2408.15050/) (2024)
- [SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics](https://mlsanity.com/paper/2410.01946/) (2024)
- [KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation](https://mlsanity.com/paper/2307.01878/) (2023)
- [ILCiteR: Evidence-grounded Interpretable Local Citation Recommendation](https://mlsanity.com/paper/2403.08737/) (2024)
- [Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation](https://mlsanity.com/paper/2104.09765/) (2021)
- [DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning](https://mlsanity.com/paper/2502.00305/) (2025)
- [PromptAL: Sample-Aware Dynamic Soft Prompts for Few-Shot Active Learning](https://mlsanity.com/paper/2507.16424/) (2025)

## Abstract (arXiv)

Hierarchical text classification, which aims to classify text documents into a given hierarchy, is an important task in many real-world applications. Recently, deep neural models are gaining increasing popularity for text classification due to their expressive power and minimum requirement for feature engineering. However, applying deep neural networks for hierarchical text classification remains challenging, because they heavily rely on a large amount of training data and meanwhile cannot easily determine appropriate levels of documents in the hierarchical setting. In this paper, we propose a weakly-supervised neural method for hierarchical text classification. Our method does not require a large amount of training data but requires only easy-to-provide weak supervision signals such as a few class-related documents or keywords. Our method effectively leverages such weak supervision signals to generate pseudo documents for model pre-training, and then performs self-training on real unlabeled data to iteratively refine the model. During the training process, our model features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism. Experiments on three datasets from different domains demonstrate the efficacy of our method compared with a comprehensive set of baselines.