PARALLEL SEMI-SUPERVISED LDA (pSSLDA)
Version 0.1

David Andrzejewski ([email protected])
Department of Computer Sciences
University of Wisconsin-Madison, USA



This software implements an extension of Latent Dirichlet Allocation
(LDA) [2] which includes "topic-in-set knowledge", or z-labels [1],
allowing the user to supply (possibly noisy) labels for specific
latent topic assignments.  Parallelized inference is done by the
Approximate Distributed (AD) [3] collapsed Gibbs sampling algorithm.

This code can also be used to do parallel inference for "standard"
LDA.

The implementation consists of Python extension modules written in C
and Cython.



BUILD/INSTALL

Building this module requires Python, NumPy, Cython, and a C compiler.
From the command-line, do:

% python setup.py install

(Note that if things are installed to non-standard locations, you may
need to make the appropriate changes in setup.py)

There is a simple example scipt showing how to use pSSLDA:

% python example/example.py



LOCAL INSTALL

If you do not have write access to your Python installation directories,
you will need to tell setup.py to install this module somewhere else.
For example:

% python setup.py install --prefix=~/local

will install the module under a subdirectory of your home directory called 
"local".

It may then be necessary to let Python know where that is by setting
the PYTHONPATH environment variable (e.g., in .bashrc or .cshrc).  For
our example this might involve adding something like the line:

setenv PYTHONPATH ~/local/lib/python2.5/site-packages



HOW TO USE

The commenting in the example.py script explains the meanings and
types of all input and return arguments.  The P parameter determines
how many parallel sampling processes to run - using a value larger
than the number of available cores is probably inadvisable.



LICENSE

This software is open-source, released under the terms of the GNU
General Public License version 3, or any later version of the GPL (see
LICENSE).



REFERENCES

[1] Andrzejewski, D. and Zhu, X. (2009).  Latent Dirichlet Allocation
with Topic-in-Set Knowledge. NAACL 2009 Workshop on Semi-supervised
Learning for NLP (NAACL-SSLNLP 2009)

[2] Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet
Allocation.  Journal of Machine Learning Research (JMLR) 3
(Mar. 2003), 993-1022.

[3] Newman, D., Asuncion, A., Smyth, P., and Welling, M.  Distributed
Algorithms for Topic Models. Journal of Machine Learning Research
(JMLR) 10 (Aug. 2009), 1801-1828.



VERSION HISTORY
0.1     Initial release