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About: MA-sLDAr is a C++ implementation of the supervised topic models with response variables provided by multiple annotators with different levels of expertise. Changes:Initial Announcement on mloss.org.
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About: MA-sLDAc is a C++ implementation of the supervised topic models with labels provided by multiple annotators with different levels of expertise. Changes:Initial Announcement on mloss.org.
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About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls. Changes:Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.
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About: Document/Text preprocessing for topic models: suite of Perl scripts for preprocessing text collections to create dictionaries and bag/list files for use by topic modelling software. Changes:Moved distribution and code across to GitHub. Changed "ldac" format to have 0 offset for word indices. Added "document frequency" (df) filtering on selection of tokens for linkTables. Playing with linkParse but its still unuseable generally.
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About: We introduces PLL, a parallel LDA learning toolbox for big topic modeling. Changes:Fix some compiling errors.
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About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline. Changes:Initial Announcement on mloss.org.
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About: Message passing for topic modeling Changes:
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About: Source code for EM approximate learning in the Latent Topic Hypertext Model. Changes:Initial Announcement on mloss.org.
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About: MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to [...] Changes:MALLET 2.0 RC4 Release Notes July 16, 2009 Major updates: An implementation of generalized expectation criteria training of MaxEnt classifiers and methods for obtaining constraints (c.f. Gregory Druck, Gideon Mann, Andrew McCallum "Learning from Labeled Features using Generalized Expectation Criteria.") PagedInstanceList has been substantially rewritten by Mike Bond. Bug fixes to topic model hyperparameter optimization and topic inference.
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About: This software implements the Dirichlet Forest (DF) Prior within the Latent Dirichlet Allocation (LDA) model. When combined with LDA, the Dirichlet Forest Prior allows the user to encode domain knowledge (must-links and cannot-links between words) into the prior on topic-word multinomials. Changes:Initial Announcement on mloss.org.
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About: This software implements the DeltaLDA model, which is a modification of the Latent Dirichlet Allocation (LDA) model. DeltaLDA can use multiple topic mixing weight priors to jointly model multiple [...] Changes:-fixed some npy_intp[] memory leaks -fixed phi normalization bug
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About: The Hidden Topic Markov Model Changes:Initial Announcement on mloss.org.
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About: GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. GibbsLDA++ is fast and is designed to analyze hidden/latent topic [...] Changes:Initial Announcement on mloss.org.
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