Open Source Text Processing Project: Stanford Open Information Extraction

Deep Learning Specialization on Coursera

Stanford Open Information Extraction

Project Website: http://nlp.stanford.edu/software/openie.shtml

Github Link: None

Description

Open information extraction (open IE) refers to the extraction of structured relation triples from plain text, such that the schema for these relations does not need to be specified in advance. For example, Barack Obama was born in Hawaii would create a triple (Barack Obama; was born in; Hawaii), corresponding to the open domain relation “was born in”. This software is a Java implementation of an open IE system as described in the paper:

Gabor Angeli, Melvin Johnson Premkumar, and Christopher D. Manning. Leveraging Linguistic Structure For Open Domain Information Extraction. In Proceedings of the Association of Computational Linguistics (ACL), 2015.

The system first splits each sentence into a set of entailed clauses. Each clause is then maximally shortened, producing a set of entailed shorter sentence fragments. These fragments are then segmented into OpenIE triples, and output by the system.


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