,Supervised topic models with weighted words:multi-label document classification

• 作 者: Yue-peng ZOU Ji-hong OUYANG Xi-ming LI
• 机 构: College of Computer Science and Technology, Jilin University, Changchun 130012, China“,”MOE Key Labo
• 出 处: 信息与电子工程前沿（英文版）
• 发表日期: 2018年4期
• 关键词: upervised topic model Multi-label classification Class frequency Labeled latent

Supervised topic modeling algorithms have been successfully applied to multi-label document classification tasks. Representative models include labeled latent Dirichlet allocation (L-LDA) and dependency-LDA. However, these models neglect the class frequency information of words (i.e., the number of classes where a word has occurred in the training data), which is significant for classification. To address this, we propose a method, namely theclass frequency weight (CF-weight), to weight words by considering the class frequency knowledge. This CF-weight is based on the intuition that a word with higher (lower) class frequency will be less (more) discriminative. In this study, the CF-weight is used to improve L-LDA and dependency-LDA. A number of experiments have been conducted on real-worldmulti-label datasets. Experimental results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.