Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (15): 193-199.DOI: 10.3778/j.issn.1002-8331.2004-0352

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Multi-label Long Text Classification Algorithm Based on Multi-level Features

WANG Haobin, HU Ping   

  1. 1.School of Software, Xi’an Jiaotong University, Xi’an 710000, China
    2.School of Management, Xi’an Jiaotong University, Xi’an 710000, China
  • Online:2021-08-01 Published:2021-07-26



  1. 1.西安交通大学 软件学院,西安 710000
    2.西安交通大学 管理学院,西安 710000


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



关键词: 多标签分类, 多级特征, 混合注意力