HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization

TL;DR

HTCInfoMax introduces a hierarchical text classification model that maximizes information between texts and labels and enforces statistical constraints on label representations, improving relevance filtering and handling label imbalance.

Contribution

It proposes a novel information maximization framework with mutual information and prior matching modules for hierarchical text classification.

Findings

Outperforms existing models on benchmark datasets

Effectively filters irrelevant information in text-label interactions

Improves label representation quality and handles imbalance

Abstract

The current state-of-the-art model HiAGM for hierarchical text classification has two limitations. First, it correlates each text sample with all labels in the dataset which contains irrelevant information. Second, it does not consider any statistical constraint on the label representations learned by the structure encoder, while constraints for representation learning are proved to be helpful in previous work. In this paper, we propose HTCInfoMax to address these issues by introducing information maximization which includes two modules: text-label mutual information maximization and label prior matching. The first module can model the interaction between each text sample and its ground truth labels explicitly which filters out irrelevant information. The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed HTCInfoMax.