HGBL: A Fine Granular Hierarchical Multi-Label Text Classification Model

Hierarchical multi-label text classification is vital for natural language processing (NLP). However, existing research rarely makes full use of the interaction between labels and text features that are crucial to hierarchical multi-label text classification. To address this issue, a novel model named hierarchy-guided BiLSTM guided contrastive learning classification (HGBL) is proposed, which successfully enhances the interaction between labels and text features by incorporating global context and embedding the idea of contrastive learning into this model. During modeling, Graphormer is adopted to model the dependencies between labels, and the bidirectional recurrent network (BiLSTM) is used to integrate global context including label features. Afterwards, the contrastive learning module embeds hierarchical awareness into the fine-tuned bidirectional encoder representations from transformers (BERT) by training the value of the loss. Experimental results on NYT, WOS and RCV1-V2 datasets show that HGBL exhibits significant competitive advantages compared with 19 competitors in terms of several indicators and can be used effectively for hierarchical multi-label text classification problems.