SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification

2026-04-17

AI Summary

This work addresses the challenge of distinguishing semantically similar sibling categories in few-shot hierarchical text classification, a problem exacerbated by insufficient domain knowledge. To this end, the authors propose a novel approach that integrates hierarchical knowledge-aware prompt tuning with sibling contrastive learning. Specifically, a hierarchical knowledge extraction module is designed to explicitly model hierarchical semantics, while a fine-grained contrastive learning mechanism is introduced among sibling categories to enhance the model’s discriminative capacity for easily confused classes. Notably, this method is the first to specifically target the differentiation of deep-level sibling categories. Experimental results on three benchmark datasets demonstrate substantial improvements over current state-of-the-art methods, effectively advancing performance in few-shot hierarchical text classification.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined tree-structured label hierarchy under data-scarce conditions. While current approaches utilize structural constraints from the label hierarchy to maintain parent-child prediction consistency, they face a critical bottleneck, the difficulty in distinguishing semantically similar sibling classes due to insufficient domain knowledge. We introduce an innovative method named Sibling Contrastive Learning with Hierarchical Knowledge-aware Prompt Tuning for few-shot HTC tasks (SCHK-HTC). Our work enhances the model's perception of subtle differences between sibling classes at deeper levels, rather than just enforcing hierarchical rules. Specifically, we propose a novel framework featuring two core components: a hierarchical knowledge extraction module and a sibling contrastive learning mechanism. This design guides model to encode discriminative features at each hierarchy level, thus improving the separability of confusable classes. Our approach achieves superior performance across three benchmark datasets, surpassing existing state-of-the-art methods in most cases. Our code is available at https://github.com/happywinder/SCHK-HTC.

Problem

• Few-shot Hierarchical Text Classification
• Sibling Classes
• Semantic Similarity
• Label Hierarchy
• Data Scarcity

Innovation

• Sibling Contrastive Learning
• Hierarchical Knowledge-aware Prompt Tuning
• Few-shot Hierarchical Text Classification
• Discriminative Feature Encoding
• Label Hierarchy