Hierarchical Multi-label Text Classification Method Based on Multi-scale Feature Extraction
A hierarchical multi-label text classification method based on multi-scale feature extraction was proposed to address the issue of current feature extraction methods in neglecting the local and global connections in text. Firstly,a multi-scale feature extraction module was designed to capture features at different scales,aiming to provide a better representation of text semantics. Secondly,the hierarchical features were embedded into the text representation to obtain a text semantic representation with label features. Finally,with the guidance of the label hierarchy,positive and negative samples were con-structed for the input text,and contrastive learning was performed to enhance the classification effective-ness. Comparative experiments were conducted on the WOS,RCV1-V2,NYT and AAPD datasets. The results indicated that the proposed model performed well in terms of the evaluation indices and ex-ceeded other mainstream models. Additionally,the hierarchical Micro-F1 and Macro-F1 indicators were proposed for hierarchical classification,and the effectiveness of the model was evaluated.
hierarchical multi-label text classificationmulti-scale feature extractioncontrastive learninghierarchical Micro-F1hierarchical Macro-F1
万方数据
维普
