CSE 250 B Assignment 3 Report
نویسندگان
چکیده
Latent Dirichlet Allocation (LDA) is a probabilistic, generative model designed to discover latent topics in text corpora, and it can be learned by collapsed Gibbs sampling. In this report, we evaluate the effectiveness of LDA by experiments on two dataset, Classic400 and BBC. We discuss related issues in Gibbs sampling, including goodness-of-fit criteria, parameter tuning, convergence, etc., and then analyze the experiment results. We showed that LDA is effective in modeling topics in a corpus using both clustering accuracy and VI-distance measures.
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