  Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for
exploring document collections. Because of the increasing prevalence of large
datasets, there is a need to improve the scalability of inference of LDA. In
this paper, we propose a technique called ~\emph{MapReduce LDA} (Mr. LDA) to
accommodate very large corpus collections in the MapReduce framework. In
contrast to other techniques to scale inference for LDA, which use Gibbs
sampling, we use variational inference. Our solution efficiently distributes
computation and is relatively simple to implement. More importantly, this
variational implementation, unlike highly tuned and specialized
implementations, is easily extensible. We demonstrate two extensions of the
model possible with this scalable framework: informed priors to guide topic
discovery and modeling topics from a multilingual corpus.
