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gensim -- Python Framework for Topic Modelling
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Gensim is a Python library for *Vector Space Modelling* with very large corpora.
Target audience is the *Natural Language Processing* (NLP) community.
Features
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* All algorithms are **memory-independent** w.r.t. the corpus size (can process input larger than RAM),
* **Intuitive interfaces**
* easy to plug in your own input corpus/datastream (trivial streaming API)
* easy to extend with other Vector Space algorithms (trivial transformation API)
* Efficient implementations of popular algorithms, such as online **Latent Semantic Analysis**,
**Latent Dirichlet Allocation** or **Random Projections**
* **Distributed computing**: can run *Latent Semantic Analysis* and *Latent Dirichlet Allocation* on a cluster of computers.
* Extensive `HTML documentation and tutorials `_.
If this feature list left you scratching your head, you can first read more about the `Vector
Space Model `_ and `unsupervised
document analysis `_ on Wikipedia.
Installation
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This software depends on `NumPy and Scipy `_, two Python packages for scientific computing.
You must have them installed prior to installing `gensim`.
The simple way to install `gensim` is::
sudo easy_install gensim
Or, if you have instead downloaded and unzipped the `source tar.gz `_ package,
you'll need to run::
python setup.py test
sudo python setup.py install
For alternative modes of installation (without root priviledges, development
installation, optional install features), see the `documentation `_.
This version has been tested under Python 2.5 and 2.6, but should run on any 2.5 <= Python < 3.0.
Documentation
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Manual for the gensim package is available in `HTML `_. It
contains a walk-through of all its features and a complete reference section.
It is also included in the source distribution package.
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Gensim is open source software, and has been released under the
`GNU LPGL license `_.
Copyright (c) 2010 Radim Rehurek