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- 453 creator jordan-boyd-graber.
- 453 creator ke-zhai.
- 453 creator nima-asadi.
- 453 type InProceedings.
- 453 label "Using Variational Inference and MapReduce to Scale Topic Modeling".
- 453 sameAs 453.
- 453 abstract "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.".
- 453 hasAuthorList authorList.
- 453 isPartOf proceedings.
- 453 keyword "lda".
- 453 keyword "mapreduce".
- 453 keyword "multilingual".
- 453 keyword "online inference".
- 453 keyword "scale".
- 453 keyword "topic modeling".
- 453 keyword "variational inference".
- 453 title "Using Variational Inference and MapReduce to Scale Topic Modeling".