A Label Information Aware Model for Multi-label Text Classification

Multi-label text classification (MLTC) refers to that each document is associated with more than one label at the same time, which attract much attention from researchers in both academia and industry. Existing methods have difficulties in determining label-related components from documents, which cannot effectively establish the association between textual features and label information. In fact, there are some label information, such as label semantic information and co-occurrence relations among labels, could be used to improve the performance on multi-label text classification. In this paper, we propose a label information aware model to utilize these information. Our model makes use of label semantic information to determine label-related components from textual features for obtaining the label-specific textual presentation for each sample, and then take advantages of co-occurrence relations among labels to construct interaction among label-specific textual presentation. The superiority of our model has been proved through comparing our method with several existing models on two datasets.