document: Mr. LDA : a flexible large scale topic modeling package using variational inference in MapReduce 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 for LDA . In this paper , we introduce a novel and flexible large scale topic modeling package in MapReduce ( Mr. LDA ) . As opposed to other techniques which use Gibbs sampling , our proposed framework uses variational inference , which easily fits into a distributed environment . More importantly , this variational implementation , unlike highly tuned and specialized implementations based on Gibbs sampling , is easily extensible . We demonstrate two extensions of the models possible with this scalable framework : informed priors to guide topic discovery and extracting topics from a multilingual corpus . We compare the scalability of Mr. LDA against Mahout , an existing large scale topic modeling package . Mr. LDA out-performs Mahout both in execution speed and held-out likelihood .
doc_bio_tags: O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O