Abstract: In the current multi-label text classification tasks, the following two problems are mainly faced: (1) Emphasis is placed on the learning of text representation, and the modeling of the association information between labels is insufficient; (2) Although label association information is used to improve multi-label classification tasks, its modeling of label association relies too much on manually predefined external knowledge, and the acquisition cost of external knowledge is high, which limits its practical application. To solve the above problems, this paper proposes a label association learning module for multi-label text classification, called Corrective-Net. The module can automatically learn label association information in data without relying on external knowledge. At the same time, it can also use label association information to modify the initial prediction of the basic classification module, so that the final prediction takes into account semantic information and label association information, so as to obtain more accurate multi-label prediction. A large number of experiments on AAPD and SO data sets show the universality and effectiveness of Corrective Net. The effects of corrective label corrections on the performance of each label are analyzed. Explicit label association information is obtained and visualized.

Key words: label association, label correction, multi-label, text classification, visualization