Abstract
Recently, supervised topic modeling approaches have received considerable attention. However, the representative labeled latent Dirichlet allocation (L-LDA) method has a tendency to over-focus on the pre-assigned labels, and does not give potentially lost labels and common semantics sufficient consideration. To overcome these problems, we propose an extension of L-LDA, namely supervised labeled latent Dirichlet allocation (SL-LDA), for document categorization. Our model makes two fundamental assumptions, i.e., Prior 1 and Prior 2, that relax the restriction of label sampling and extend the concept of topics. In this paper, we develop a Gibbs expectation-maximization algorithm to learn the SL-LDA model. Quantitative experimental results demonstrate that SL-LDA is competitive with state-of-the-art approaches on both single-label and multi-label corpora.
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In fact, \(Dir^{\prime }(\overrightarrow \alpha ,{y_{d}})\) is a pseudo-PDF, because \({\int }_{- \infty }^{\infty } Dir^{\prime }(\overrightarrow \alpha ,{y_{d}}) = \) 1−𝜃 ∗. Here, it is used to sample the absence topic components in \(\overrightarrow {{{\Lambda }_{d}}}\).
Results for the Yahoo! Health collection are very similar.
We set K = 100 for Yahoo! Arts and K = 240 for RCV1-v2.
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Acknowledgments
This work was supported by National Nature Science Foundation of China (NSFC) under the Grant No. 61170092, 61133011, and 61103091.
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Li, X., Ouyang, J., Zhou, X. et al. Supervised labeled latent Dirichlet allocation for document categorization. Appl Intell 42, 581–593 (2015). https://doi.org/10.1007/s10489-014-0595-0
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DOI: https://doi.org/10.1007/s10489-014-0595-0