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"
relation.
- It's aimed at:
- users needing to perform Information Extraction on a large dataset.
- scientists wanting to experiment with new IE algorithms.
- 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-jreThen simply install with pip:
pip install iepyFull details about the installation is available on the Read the Docs page.
The full documentation is available on Read the Docs.
IEPY is © 2014 Machinalis in collaboration with the NLP Group at UNC-FaMAF. 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