LMFN: Label-Aware Multi-Semantic Fusion Network for Multi-Label Text Classification

The multi-label text classification (MLTC) task involves associating text data with multiple relevant labels. However, previous studies often overlooked the co-occurrence information of labels within text, resulting in the inability to distinguish similar labels. Moreover, these studies have underestimated the importance of label node initialization. To tackle these challenges, we propose a Label-aware Multi-semantic Fusion Network (LMFN). Our approach employs label-guided attention to learn text representations closely aligned with labels. Then, in order to obtain more refined semantic representation, the word embedding matrix is introduced to integrate features from diverse sources. Concurrently, we use joint learning network to extract label features. We first initialize label nodes and use multi-layer GCNs to capture the dependencies and higher-order information between labels. Following this, cross-attention is utilized to effectively integrate label features with internal semantics and reveal latent correlations. Comprehensive experiments on two standard datasets show that our proposed model LMFN outperforms existing methods.