Metadata-Version: 1.0
Name: gensim
Version: 0.3.0
Summary: Python Framework for Topic Modeling
Home-page: http://nlp.fi.muni.cz/projekty/gensim
Author: Radim Rehurek
Author-email: radimrehurek@seznam.cz
License: LGPL
Download-URL: http://pypi.python.org/pypi/gensim
Description: 
        Gensim is a Python framework for unsupervised learning from raw, unstructured digital texts.
        It provides a framework for learning hidden (*latent*) corpus structure.
        Once found, documents can be succinctly expressed in terms of this structure, queried
        for topical similarity and so on.
        
        Gensim includes the following features:
        * Memory independence -- there is no need for the whole text corpus (or any
        intermediate term-document matrices) to reside fully in RAM at any one time.
        * Provides implementations for several popular topic inference algorithms,
        including Latent Semantic Analysis (LSA, LSI) and Latent Dirichlet Allocation (LDA),
        and makes adding new ones simple.
        * Contains I/O wrappers and converters around several popular data formats.
        * Allows similarity queries across documents in their latent, topical representation.
        
        The principal design objectives behind gensim are:
        1. Straightforward interfaces and low API learning curve for developers,
        facilitating modifications and rapid prototyping.
        2. Memory independence with respect to the size of the input corpus; all intermediate
        steps and algorithms operate in a streaming fashion, processing one document
        at a time.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
