mallet-package: An R Wrapper for the Java Mallet Topic Modeling Toolkit
In mallet: An R Wrapper for the Java Mallet Topic Modeling Toolkit
mallet-package R Documentation
An R Wrapper for the Java Mallet Topic Modeling Toolkit
Description
An R interface for the Java Machine Learning for Language Toolkit (mallet)
<http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such
as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects,
run the Java implementation of mallet directly in R, and extract results
as R objects. The Mallet toolkit has many functions, this wrapper focuses
on the topic modeling sub-package written by David Mimno. The package uses
the rJava package to connect to a JVM.
References
The model, Latent Dirichlet allocation (LDA):
David M Blei, Andrew Ng, Michael Jordan. Latent Dirichlet Allocation. J. of Machine Learning Research, 2003.
The Java toolkit:
Andrew Kachites McCallum. The Mallet Toolkit. 2002.
Details of the fast sparse Gibbs sampling algorithm:
Limin Yao, David Mimno, Andrew McCallum. Streaming Inference for Latent Dirichlet Allocation. KDD, 2009.
Hyperparameter optimization:
Hanna Wallach, David Mimno, Andrew McCallum. Rethinking LDA: Why Priors Matter. NIPS, 2010.
mallet documentation built on July 20, 2022, 5:08 p.m.
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| mallet-package | R Documentation |
An R Wrapper for the Java Mallet Topic Modeling Toolkit
Description
An R interface for the Java Machine Learning for Language Toolkit (mallet) <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM.
References
The model, Latent Dirichlet allocation (LDA): David M Blei, Andrew Ng, Michael Jordan. Latent Dirichlet Allocation. J. of Machine Learning Research, 2003.
The Java toolkit: Andrew Kachites McCallum. The Mallet Toolkit. 2002.
Details of the fast sparse Gibbs sampling algorithm: Limin Yao, David Mimno, Andrew McCallum. Streaming Inference for Latent Dirichlet Allocation. KDD, 2009.
Hyperparameter optimization: Hanna Wallach, David Mimno, Andrew McCallum. Rethinking LDA: Why Priors Matter. NIPS, 2010.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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