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Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they… (More)

Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of… (More)

- Sanjeev Arora, Rong Ge, +5 authors Michael Zhu
- ICML
- 2013

Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model inference have been based on a maximum likelihood… (More)

- Hanna M. Wallach, David M. Mimno, Andrew McCallum
- NIPS
- 2009

Implementations of topic models typically use symmetric Dirichlet priors with fixed concentration parameters, with the implicit assumption that such “smoothing parameters” have little practical… (More)

- Limin Yao, David M. Mimno, Andrew McCallum
- KDD
- 2009

Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of training documents… (More)

- David M. Mimno, Andrew McCallum
- UAI
- 2008

Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper… (More)

A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators… (More)

- David M. Mimno, Matthew D. Hoffman, David M. Blei
- ICML
- 2012

We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a… (More)

- Tobias Schnabel, Igor Labutov, David M. Mimno, Thorsten Joachims
- EMNLP
- 2015

We present a comprehensive study of evaluation methods for unsupervised embedding techniques that obtain meaningful representations of words from text. Different evaluations result in different… (More)

- David M. Mimno, Wei Li, Andrew McCallum
- ICML
- 2007

The four-level pachinko allocation model (PAM) (Li & McCallum, 2006) represents correlations among topics using a DAG structure. It does not, however, represent a nested hierarchy of topics, with… (More)