An open source tool for Information Extraction in PythonPosted by Elías Andrawos 2 years, 4 months ago Comments
We're happy to announce that IEPY 0.9.4 was released!!
|- Added multicore preprocess|
|- Added support for Stanford 3.5.2 preprocess models|
- It’s aimed at:
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.
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 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.
- A web-based user interface that:
- Allows layman users to control some aspects of IEPY.
- Allows decentralization of human input.
Demo using Techcrunch articles: http://iepycrunch.machinalis.com/
Github: https://github.com/machinalis/iepy PyPi: https://pypi.python.org/