IEPY is an open source tool for 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"
- It's aimed at:
- A corpus annotation tool with a web-based UI
- An active learning relation extraction tool pre-configured with convenient defaults.
- A rule based relation extraction tool 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
- An easily hack-able active learning core, ideal for scientist wanting to experiment with new algorithms.
Install the required packages:
sudo apt-get install build-essential python3-dev liblapack-dev libatlas-dev gfortran openjdk-7-jre
Then simply install with pip:
pip install iepy
Full details about the installation is available on the Read the Docs page.
Running the tests
If you are contributing to the project and want to run the tests, all you have to do is:
- Make sure your JAVAHOME is correctly set. Read more about it here
- In the root of the project run nosetests
The full documentation is available on Read the Docs.
- Rafael Carrascosa <email@example.com> (rafacarrascosa at github)
- Javier Mansilla <firstname.lastname@example.org> (jmansilla at github)
- Gonzalo García Berrotarán <email@example.com> (j0hn at github)
- Franco M. Luque <firstname.lastname@example.org> (francolq at github)
- Daniel Moisset <email@example.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