Mining Knowledge within Categories in Global and Local Fashion for Multi-Label Text Classification

Multi-label text classification (MLTC) is an important task in natural language processing, which assigns multiple labels to each text in the dataset. Typical method like Binary Relevance (BR) is arguably the most intuitive solution for the task. It works by decomposing the multi-label learning task into a number of independent binary learning tasks while ignoring the correlation between labels. Recently, neural network models attract much attention. Researchers view the MLTC task as a sequence generation problem. Although some new methods based on generative model (e.g. sequence-to-sequence), such as novel decoder structure and various attention mechanisms, can improve the performance. These methods still have some short-comings, such as unreasonable loss function, unclear ordering of target labels. To address these limitations, we propose a simple and effective novel model, which combines the merits of neural network and BRs methods. Our model also takes into account the categories and levels of labels. We decompose the MLTC problem to binary classification, together with global and local extractor to avoid the impact of label ordering and cumulative error. Experimental results show that our model achieves an improvement of 3.0% micro-F1 and a reduction of 6.0% hamming loss on AAPD dataset compared with the state-of-the-art work. And obtained good performance on RCV1-V2 dataset.