Short Text Classification Model Based on Label-Feature Augmentation and Multi-Attention Mechanism

In short text classification tasks, existing studies primarily focus on extracting textual information while neglecting the semantics and dependencies among category labels. In practical applications, label information can effectively alleviate the semantic sparsity issue of short texts. This paper proposes a short text classification model that integrates label-feature augmentation with a multi-attention mechanism to deeply explore the dependencies between label semantics and text classification. First, the model expands labels via external knowledge bases to augment label representations. Meanwhile, for preprocessed short texts, entity linking is implemented to obtain corresponding entity information, which is then employed to retrieve relevant conceptual information from external knowledge bases, thereby uncovering deeper semantic content. Furthermore, a multi-attention mechanism is designed to learn the feature relationships among different embeddings generated by pre-trained models: a text self-attention module extracts key text representations, a label-text attention module captures text-related label features, an entity-text attention module retrieves associated entity information, and a concept-text attention module extracts conceptual-level semantics. Finally, these features are fused and input into a fully connected network to capture multi-level semantic information and complete short text classification. Experimental results on public benchmark datasets demonstrate that the proposed model significantly outperforms comparison methods on short text classification tasks and achieves superior performance.