Multi-label text classification via joint learning from label embedding and label correlation

Abstract

For the multi-label text classification problems with many classes, many existing multi-label classification algorithms become infeasible or suffer an unaffordable cost. Some researches hence perform the Label Space Dimension Reduction(LSDR) to solve this problem, but a number of methods ignore the sequence information of texts and the label correlation in the original label space, and treat the label as a meaningless multi-hot vector. In this paper, we put forward a multi-label text classification algorithm LELC(joint learning from Label Embedding and Label Correlation) based on the multi-layer attention and label correlation to solve the issue of multi-label text classification with a large number of class labels. Specifically, we firstly extract features through Bidirectional Gated Recurrent Unit Network(Bi-GRU), multi-layer attention and linear layers. Bi-GRU will capture the content information and sequence information of the text at the same time, and the attention mechanism can help us select the valid features related to labels. Then, we use matrix factorization to perform LSDR, and consider label correlation of the original label space in this process, which allows us to implicitly encode the latent space and simplify the model learning. Finally, Deep Canonical Correlation Analysis(CCA) technology is exploited to couple features and the latent space in an end-to-end pattern, so that these two can influence each other to learn the mapping of feature space to latent space. Experiments on 11 real-world datasets show the comparability between our proposed model and the state-of-the-art methods.