Multi-label Text Classification by Fusing Pseudo-label Generation and Data Augmentation

Aiming at the problem that the multi-label text classification algorithm ignores the noise label and lacks the combination incentive of true and false, which leads to the weak robustness of the model and the poor classification effect, a cascaded BiLSTM- Capsule Network for Noisy Multi-Label Text Classification, CBCN, is proposed for short text datasets. The algorithm uses capsule neural network to extract spatial semantic features between tokens, and uses multi-layer cascaded BiLSTM to perform multi-level feature extraction from input text. It utilizes noise sample training and augmentation training to distinguish real labels from noise labels, thereby achieving correct multi-label combination recognition. The accuracy of the CBCN algorithm on the two public English data sets of AAPD and RCV1-V2 reaches 89. 1% and 91. 1% . This algorithm has strong robustness and generalization performance for solving noisy multi-label text classification problems.