@article {12417,
	title = {Using Variational Inference and MapReduce to Scale Topic Modeling},
	journal = {arXiv:1107.3765},
	year = {2011},
	month = {2011/07/19/},
	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.},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing},
	url = {http://arxiv.org/abs/1107.3765},
	author = {Zhai,Ke and Jordan Boyd-Graber and Asadi,Nima}
}
@article {12415,
	title = {Syntactic Topic Models},
	journal = {arXiv:1002.4665v1},
	year = {2010},
	month = {2010/02/24/},
	abstract = {The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents. It assumes that each word is drawn from a latent topic chosen by combining document-level features and the local syntactic context. Each document has a distribution over latent topics, as in topic models, which provides the semantic consistency. Each element in the dependency parse tree also has a distribution over the topics of its children, as in latent-state syntax models, which provides the syntactic consistency. These distributions are convolved so that the topic of each word is likely under both its document and syntactic context. We derive a fast posterior inference algorithm based on variational methods. We report qualitative and quantitative studies on both synthetic data and hand-parsed documents. We show that the STM is a more predictive model of language than current models based only on syntax or only on topics.},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Mathematics - Statistics Theory},
	url = {http://arxiv.org/abs/1002.4665},
	author = {Jordan Boyd-Graber and Blei,David M.}
}
