[Topic-models] Training Classifier with multi-labeled data

Daniel Ramage dramage at cs.stanford.edu
Wed Mar 3 12:46:56 EST 2010

Hi Bin,

One option is to use Labeled LDA, 
http://www.aclweb.org/anthology/D/D09/D09-1026.pdf which constrains each 
document's topic distribution to align with the document's label space. 
  Because the per-document topics in this model are actually observed, 
it's less of a latent and more of a blatant dirichlet allocation.  It's 
competitive with an SVM baseline in our experiments, but state of the 
art discriminative models still beat it.


Liu Bin wrote:
> Dear Friends,
> Currently, my job is designing a classifier to classify some short 
> questions.
> The classifier can be trained by a collection of avialable questions 
> with labels.
> And a question can be labeled to several related categories.
> Thus, the topic models might be useful.
> However, as I have known, the LDA model is an unsupervised model.
> Is there any alternative way to use topic models to do multi-labeled 
> classification?
> Thanks!
> Yours,
> Bin
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