Python Framework for Topic Modeling
Gensim is a Python framework designed to help make the conversion of natural language texts to the Vector Space Model as simple and natural as possible.
Gensim contains algorithms for unsupervised learning from raw, unstructured digital texts, such as Latent Semantic Analysis and Latent Dirichlet Allocation. These algorithms discover hidden (latent) corpus structure. Once found, documents can be succinctly expressed in terms of this structure, queried for topical similarity and so on.
Gensim’s target audience is the NLP research community and interested general public; gensim is not meant to be a production tool for commercial environments.
This version has been tested under Python 2.5, but should run on any 2.5 <= Python < 3.0.
gensim depends on NumPy and Scipy, two Python packages for scientific computing. You need to have them installed prior to using gensim; if you don’t have them yet, you can get them from <http://www.scipy.org/Download>.
The simple way to install gensim is:
sudo easy_install gensim
If you have instead downloaded and unzipped the source tar.gz package <http://pypi.python.org/pypi/gensim>, you’ll need to run:
python setup.py test sudo python setup.py install
For alternative modes of installation (without root priviledges, development installation), see the documentation.
Manual for the gensim package is available as HTML and as PDF. It contains a walk-through of all the features and a complete reference section. It is part of the source package and it can also be viewed on the project’s homepage <http://nlp/fi.muni.cz/projekty/gensim/>.
Gensim is open source software, and has been released under GNU LPGL license.
Copyright (c) 2010 Radim Rehurek
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