Hierarchical Latent Dirichlet Allocation (HLDA)
URL: http://code.google.com/p/hlda-cpp/ License: Apache 2.0
This is a C++ re-implementation of David Blei's Hierarchical Latent Dirichlet Allocation (HLDA) topic modeling software. The model finds a hierarchy of topics, where the data determines the structure of the hierarchy. The depth of the hierarchy is fixed.
The HLDA model is described in two of David Blei's papers: http://www.cs.princeton.edu/~blei/papers/BleiGriffithsJordanTenenbaum2003.pdf http://www.cs.princeton.edu/~blei/papers/BleiGriffithsJordan2009a.pdf
David Blei's implementation of HLDA in C: http://www.cs.princeton.edu/~blei/downloads/hlda-c.tgz
Dependencies: This code depends on the GSL GNU Scientific Library: http://www.gnu.org/software/gsl/
Input: The input is in the following format: [number of unique words] [word id] : [count] ... There is a sample test file in the testdata directory, along with a settings file for this test data.
Other corpora in the same format can be found here:
Instructions to run the code:
input_corpus: the input file, in the format described under "Input". settings_file: a settings file. An example settings file can be found in the testdata directory.
HLDA settings file:
DEPTH the maximum depth of the hierarchy. ETA represents the expected variance of the underlying topics. GAM not used in this implementation. GEM_MEAN a parameter of the GEM distribution. It shows the proportion of general words relative to specific words. GEM_SCALE a parameter of the GEM distribution. It shows how strictly documents should follow the general versus specific word proportions. SCALING_SHAPE scaling parameter for the G prior. SCALING_SCALE scaling parameter for the G prior. SAMPLE_ETA if the ETA parameter is sampled. SAMPLE_GEM if the GEM parameters are sampled.
This work was done in the context of the RENDER project (http://www.render-project.eu/).