GV

GeezerVison

The World Wide Web, restored to 800 pixels of glory.

Home|About|Gaming cabinet|Archives|How it works|Starter sites

On this site

  • Sign in
  • ActionsCI/CD and automation
  • CodespacesCloud development environments
  • All Features
  • Documentation
  • Training
  • Certifications
  • Build with Copilot
  • Customize Copilot
  • Integrations
  • Trending repos
  • GitHub Sponsors
  • Developer articles
  • Enterprises

This view

  • Original page
  • New address

GeezerVison is showing github.com — GitHub - machinalis/iepy: Information Extraction in Python

BranchesTags
Open more actions menu

Latest commit

 

History

1,782 Commits
1,782 Commits

Folders and files

NameName
Last commit message
Last commit date
docs
docs
 
 
examples/birthdate
examples/birthdate
 
 
iepy
iepy
 
 
scripts
scripts
 
 
tests
tests
 
 
.gitignore
.gitignore
 
 
AUTHORS
AUTHORS
 
 
ChangeLog
ChangeLog
 
 
LICENSE
LICENSE
 
 
LICENSE_details.txt
LICENSE_details.txt
 
 
MANIFEST.in
MANIFEST.in
 
 
README.rst
README.rst
 
 
lex_features.py
lex_features.py
 
 
setup.py
setup.py
 
 

Repository files navigation

IEPY

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.

Features

  • 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.

Installation

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

Learn more

The full documentation is available on Read the Docs.

Authors

IEPY is © 2014 Machinalis in collaboration with the NLP Group at UNC-FaMAF. Its primary authors are:

  • Rafael Carrascosa <[email protected]> (rafacarrascosa at github)
  • Javier Mansilla <[email protected]> (jmansilla at github)
  • Gonzalo García Berrotarán <[email protected]> (j0hn at github)
  • Franco M. Luque <[email protected]> (francolq at github)
  • Daniel Moisset <[email protected]> (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

About

Information Extraction in Python

Resources

Readme
BSD-3-Clause license
Activity
Custom properties

Stars

904 stars

Watchers

63 watching

Forks

181 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

You are visitor 01402503 — GeezerVison does not store the pages you look up.
Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required.