Information Extraction in Python
Project Website: None
Github Link: https://github.com/machinalis/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.
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.