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In text mining, topic models are a type of probabilistic generative modelsfor inferring latent semantic topics from text corpus. One of the most popularinference approaches to topic models is perhaps collapsed Gibbs sampling (CGS),which typically samples one single topic label for each observed document-wordpair. In this paper, we aim at improving the inference of CGS for topic models.We propose to leverage state augmentation technique by maximizing the number oftopic samples to infinity, and then develop a new inference approach, calledinfinite latent state replication (ILR), to generate robust soft topicassignment for each given document-word pair. Experimental results on thepublicly available datasets show that ILR outperforms CGS for inference ofexisting established topic models.

Improving the Inference of Topic Models via Infinite Latent State Replications