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.
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Project supported by the National Natural Science Foundation of China (No. 61602204)
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Zou, Yp., Ouyang, Jh. & Li, Xm. Supervised topic models with weighted words: multi-label document classification. Frontiers Inf Technol Electronic Eng 19, 513–523 (2018). https://doi.org/10.1631/FITEE.1601668
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DOI: https://doi.org/10.1631/FITEE.1601668
Key words
- Supervised topic model
- Multi-label classification
- Class frequency
- Labeled latent Dirichlet allocation (L-LDA)
- Dependency-LDA