潜在语义聚类与图交互驱动的少样本文本分类OA

To address the issues of scarce tail-class samples,insufficient fitting of pre-trained language models,and the reliance on global static structures in existing graph-based methods for few-shot long-tail classification,a text classification method based on latent semantic clustering and graph interaction is proposed.Firstly,the language model is semantically warmed up using label descriptions to align texts and labels into a unified representation space.Secondly,learnable latent topic nodes are constructed through unsupervised clustering to serve as semantic relays between texts and labels,thereby alleviating the semantic sparsity of tail categories.Finally,an independent heterogeneous subgraph is dynamically constructed for each text instance,local information is aggregated via a graph neural network,and text representations are enhanced through a cross-attention mechanism.Experimental results on 5 bench-mark datasets demonstrate that after integrating the proposed method with 3 fine-tuned baseline models(BERT,RoBERTa,and DeBERTa),the macro F1 score of the models is improved by 2.91 percentage points,and the macro tail F1 score is improved by 3.21 percentage points,showing a more significant en-hancement in tail performance.Furthermore,its memory overhead is significantly lower than that of global graph methods,offering an effective and efficient solution for few-shot imbalanced classification.