# HDLTex: Hierarchical Deep Learning for Text Classification

arXiv: https://arxiv.org/abs/1709.08267 · HTML: https://mlsanity.com/paper/1709.08267/

## Key facts

- Cited by 106 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2017.

## Built on by (most cited first, 30 of 106)

- [MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series](https://mlsanity.com/paper/2405.19327/) (2024)
- [Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification](https://mlsanity.com/paper/2203.03825/) (2022)
- [Recent Advances of Foundation Language Models-based Continual Learning: A Survey](https://mlsanity.com/paper/2405.18653/) (2024)
- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification](https://mlsanity.com/paper/2204.13413/) (2022)
- [Rehearsal-Free Modular and Compositional Continual Learning for Language Models](https://mlsanity.com/paper/2404.00790/) (2024)
- [HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization](https://mlsanity.com/paper/2104.05220/) (2021)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Can a Fruit Fly Learn Word Embeddings?](https://mlsanity.com/paper/2101.06887/) (2021)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification](https://mlsanity.com/paper/2406.17534/) (2024)
- [Multi-granular Legal Topic Classification on Greek Legislation](https://mlsanity.com/paper/2109.15298/) (2021)
- [Exploiting Global and Local Hierarchies for Hierarchical Text Classification](https://mlsanity.com/paper/2205.02613/) (2022)
- [Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks](https://mlsanity.com/paper/2405.02225/) (2024)
- [Constrained Sequence-to-Tree Generation for Hierarchical Text Classification](https://mlsanity.com/paper/2204.00811/) (2022)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [A Transformer-based Framework for POI-level Social Post Geolocation](https://mlsanity.com/paper/2211.01336/) (2022)
- [Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification](https://mlsanity.com/paper/2305.16885/) (2023)
- [Learning to Route for Dynamic Adapter Composition in Continual Learning with Language Models](https://mlsanity.com/paper/2408.09053/) (2024)
- [Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale?](https://mlsanity.com/paper/2412.05137/) (2024)
- [Hierarchical Multi-Label Classification of Scientific Documents](https://mlsanity.com/paper/2211.02810/) (2022)
- [DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling](https://mlsanity.com/paper/2305.19395/) (2023)
- [Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning](https://mlsanity.com/paper/2104.01666/) (2021)
- [SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions](https://mlsanity.com/paper/2506.00643/) (2025)
- [Automated Identification of Toxic Code Reviews Using ToxiCR](https://mlsanity.com/paper/2202.13056/) (2022)
- [A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification](https://mlsanity.com/paper/2305.15282/) (2023)
- [GORAG: Graph-based Online Retrieval Augmented Generation for Dynamic Few-shot Social Media Text Classification](https://mlsanity.com/paper/2501.02844/) (2025)
- [AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs](https://mlsanity.com/paper/2511.05549/) (2025)
- [Video-based Hierarchical Species Classification for Longline Fishing Monitoring](https://mlsanity.com/paper/2102.03520/) (2021)
- [Hierarchical Text Classification As Sub-Hierarchy Sequence Generation](https://mlsanity.com/paper/2111.11104/) (2021)

## Abstract (arXiv)

The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of these traditional classifiers has degraded as the number of documents has increased. This is because along with this growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.