HyILR: Hyperbolic Instance-Specific Local Relationships for Hierarchical Text Classification

Recent approaches to Hierarchical Text Classification (HTC) rely on capturing the global label hierarchy, which contains static and often redundant relationships.Instead, the hierarchical relationships within the instance-specific set of positive labels are more important, as they focus on the relevant parts of the hierarchy.These localized relationships can be modeled as a semantic alignment between the text and its positive labels within the embedding space.However, without explicitly encoding the global hierarchy, achieving this alignment directly in Euclidean space is challenging, as its flat geometry does not naturally support hierarchical relationships.To address this, we propose Hyperbolic Instance-Specific Local Relationships (HyILR), which models instancespecific relationships using the Lorentz model of hyperbolic space.Text and label features are projected into hyperbolic space, where a contrastive loss aligns text with its labels.This loss is guided by a hierarchy-aware negative sampling strategy, ensuring the selection of structurally and semantically relevant negatives.By leveraging hyperbolic geometry for this alignment, our approach inherently captures hierarchical relationships and eliminates the need for global hierarchy encoding.Experimental results on four benchmark datasets validate the superior performance of HyILR over baseline methods. 1