Abstract: 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 the class 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-world multi-label datasets. Experimental results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.

Key words: Supervised topic model, Multi-label classification, Class frequency, Labeled latent Dirichlet allocation (L-LDA), Dependency-LDA

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