关键词: 多标签分类, 多级特征, 混合注意力

Abstract:

For the existing multi-label classification algorithm has ignored the endogenous relationship between the labels, In this paper, the multi-label classification problem is converted into a sequence generation problem, and the symbiotic relationship between the labels is fully considered. Based on the Seq2Seq model, text features are extracted from two dimensions：word level and semantic level. By improving the feature extraction module, encoder structure, mixed attention mechanism, and decoder prediction part, a multi-label classification algorithm based on multi-level features and mixed attention mechanism is proposed. The effectiveness of the algorithm is verified on the three data sets of Zhihu, RCV1-V2 and AAPD and compared with existing algorithms. The proposed algorithm is superior to other algorithms in F1 value, recall rate and Hamming loss.

Key words: multi-label classification, multi-level features, mixed attention