Papers with Hierarchical Text Classification
Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer) (2024.emnlp-industry)
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| Challenge: | Existing methods for Hierarchical Text Classification (HTC) are expensive and require explicit injection of the hierarchy, verbalizers, and/or prompt engineering. |
| Approach: | They propose a hierarchical text classification system that uses a single classifier to predict one or more topics using differentiable prompts and labels that are learnt through backpropagation. |
| Outcome: | The proposed model outperforms existing models on several benchmarks that span a range of topics consistently. |
HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification (2025.acl-srw)
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| Challenge: | Hierarchical text classification models rely on capturing global label hierarchy, which contains static and redundant relationships. |
| Approach: | They propose a method which captures hierarchical relationships without encoding global hierarchy . they use hyperbolic geometry to model instance-specific local relationships using Lorentz model . |
| Outcome: | The proposed model captures hierarchical relationships without encoding global hierarchy . the proposed model is superior to baseline methods on four benchmark datasets . |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
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| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
HierPrompt: Zero-Shot Hierarchical Text Classification with LLM-Enhanced Prototypes (2025.findings-emnlp)
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| Challenge: | Existing methods for Hierarchical Text Classification are based on prototypes, but do not perform well due to ambiguity and impreciseness of category names. |
| Approach: | They propose a method that leverages hierarchy-aware prompts to instruct LLM to produce more representative and informative prototypes. |
| Outcome: | The proposed method outperforms existing methods on three benchmark datasets. |
Enhancing Hierarchical Text Classification through Knowledge Graph Integration (2023.findings-acl)
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| Challenge: | Existing approaches to hierarchical text classification are limited by lack of domain knowledge, which leads to mistakes in a variety of situations. |
| Approach: | They propose a Knowledge-enabled Hierarchical Text Classification model which integrates knowledge graphs into HTC to address the knowledge limitations of traditional methods. |
| Outcome: | The proposed model integrates knowledge graphs into the hierarchical text classification process, addressing the knowledge limitations of traditional methods. |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
Label Augmentation for Zero-Shot Hierarchical Text Classification (2024.acl-long)
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| Challenge: | Hierarchical Text Classification is a difficult problem due to the lack of labeled data and the cost of manually annotating data samples. |
| Approach: | They propose a method that uses a Large Language Model to augment the deepest layer of the labels hierarchy to enhance its specificity. |
| Outcome: | The proposed method achieves state-of-the-art on four public datasets and a strong correlation between the metric values and the classification performance. |
Well Begun Is Half Done: An Implicitly Augmented Generative Framework with Distribution Modification for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical Text Classification (HTC) is a challenging task which aims to extract the labels in a tree structure corresponding to a given text. |
| Approach: | They propose an explicit-agmented-generativ-e framework with distribution modification for hierarchical text classification. |
| Outcome: | The proposed framework improves on the initial distributions of tail classes and avoids misinterpreting predictions on unbalanced data. |