  The generative aspect model is an extension of the multinomial model for text
that allows word probabilities to vary stochastically across documents.
Previous results with aspect models have been promising, but hindered by the
computational difficulty of carrying out inference and learning. This paper
demonstrates that the simple variational methods of Blei et al (2001) can lead
to inaccurate inferences and biased learning for the generative aspect model.
We develop an alternative approach that leads to higher accuracy at comparable
cost. An extension of Expectation-Propagation is used for inference and then
embedded in an EM algorithm for learning. Experimental results are presented
for both synthetic and real data sets.
