Metadata-Version: 1.1
Name: iepy
Version: 0.9.5
Summary: Information Extraction framework in Python
Home-page: https://github.com/machinalis/iepy
Author: Rafael Carrascosa, Javier Mansilla, Gonzalo García Berrotarán, Daniel Moisset, Franco M. Luque
Author-email: UNKNOWN
License: BSD
Description: IEPY
        ====
        
        IEPY is an open source tool for
        `Information Extraction <http://en.wikipedia.org/wiki/Information_extraction>`_
        focused on Relation Extraction.
        
        To give an example of Relation Extraction, if we are trying to find a
        birth date in:
        
            `"John von Neumann (December 28, 1903 – February 8, 1957) was a Hungarian and
            American pure and applied mathematician, physicist, inventor and polymath."`
        
        then IEPY's task is to identify "``John von Neumann``" and
        "``December 28, 1903``" as the subject and object entities of the "``was born in``"
        relation.
        
        It's aimed at:
            - `users <http://iepy.readthedocs.org/en/latest/active_learning_tutorial.html>`_
              needing to perform Information Extraction on a large dataset.
            - `scientists <http://iepy.readthedocs.org/en/latest/how_to_hack.html>`_
              wanting to experiment with new IE algorithms.
        
        Features
        --------
        
            - `A corpus annotation tool <http://iepy.readthedocs.org/en/latest/corpus_labeling.html>`_
              with a `web-based UI <http://iepy.readthedocs.org/en/latest/corpus_labeling.html#document-based-labeling>`_
            - `An active learning relation extraction tool <http://iepy.readthedocs.org/en/latest/active_learning_tutorial.html>`_
              pre-configured with convenient defaults.
            - `A rule based relation extraction tool <http://iepy.readthedocs.org/en/latest/rules_tutorial.html>`_
              for cases where the documents are semi-structured or high precision is required.
            - A web-based user interface that:
                - Allows layman users to control some aspects of IEPY.
                - Allows decentralization of human input.
            - A shallow entity ontology with coreference resolution via `Stanford CoreNLP <http://nlp.stanford.edu/software/corenlp.shtml>`_
            - `An easily hack-able active learning core <http://iepy.readthedocs.org/en/latest/how_to_hack.html>`_,
              ideal for scientist wanting to experiment with new algorithms.
        
        Installation
        ------------
        
        Install the required packages:
        
        .. code-block:: bash
        
            sudo apt-get install build-essential python3-dev liblapack-dev libatlas-dev gfortran openjdk-7-jre
        
        Then simply install with **pip**:
        
        .. code-block:: bash
        
            pip install iepy
        
        Full details about the installation is available on the
        `Read the Docs <http://iepy.readthedocs.org/en/latest/installation.html>`__ page.
        
        Learn more
        ----------
        
        The full documentation is available on `Read the Docs <http://iepy.readthedocs.org/en/latest/>`__.
        
        
        Authors
        -------
        
        IEPY is © 2014 `Machinalis <http://www.machinalis.com/>`_ in collaboration
        with the `NLP Group at UNC-FaMAF <http://pln.famaf.unc.edu.ar/>`_. Its primary
        authors are:
        
         * Rafael Carrascosa <rcarrascosa@machinalis.com> (rafacarrascosa at github)
         * Javier Mansilla <jmansilla@machinalis.com> (jmansilla at github)
         * Gonzalo García Berrotarán <ggarcia@machinalis.com> (j0hn at github)
         * Franco M. Luque <francolq@famaf.unc.edu.ar> (francolq at github)
         * Daniel Moisset <dmoisset@machinalis.com> (dmoisset at github)
        
        You can follow the development of this project and report issues at
        http://github.com/machinalis/iepy
        
        You can join the mailing list `here <https://groups.google.com/forum/?hl=es-419#%21forum/iepy>`__
        
Keywords: information extraction relation detection
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3 :: Only
