Supervised topic models. Blei, D., M. and Mcauliffe, J., D.
Supervised topic models [pdf]Paper  Supervised topic models [pdf]Website  abstract   bibtex   
We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.
@article{
 title = {Supervised topic models},
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 websites = {https://papers.nips.cc/paper/3328-supervised-topic-models.pdf},
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 created = {2018-02-05T19:13:28.795Z},
 accessed = {2018-02-05},
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 abstract = {We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.},
 bibtype = {article},
 author = {Blei, David M and Mcauliffe, Jon D}
}
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