Expectation-Propogation for the Generative Aspect Model

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.

• Publication year

  2002

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  2002-08-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1301.0588
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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