An Efficient Framework by Topic Model for Multi-label Text Classification

Most existing multi-label text classification (MLTC) approaches only exploit label correlations from label pairwises or label chains. However, in the real world, features of instances have much importance for classification. In this paper, we propose a simple but efficient framework for MLTC called Hybrid Latent Dirichlet Allocation Multi-Label (HLDAML). To be specific, the topics of text features (i.e., a concrete description of documents) and the topics of label sets (i.e., a summarization of documents) can be obtained from training data by topic model before building models for multi-label classification. After that, hybrid topics can be used in existing approaches to improve the performance of MLTC. Experiments on several benchmark datasets demonstrate that the proposed framework is general and effective when taking text features and label sets into consideration simultaneously. It is also worth mentioning that we construct a new multi-label dataset called Parkinson about diagnosing parkinson disease by Traditional Chinese Medicine.