Title

Learning Relational Dependency Networks For Relation Extraction

Document Type

Conference Proceeding

Publication Date

2016

Published In

Inductive Logic Programming

Abstract

We consider the task of KBP slot filling – extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such as weak supervision, word2vec features, joint learning and the use of human advice, can be incorporated in this relational framework. We evaluate the different components in the benchmark KBP 2015 task and show that RDNs effectively model a diverse set of features and perform competitively with current state-of-the-art relation extraction methods.

Published By

Springer

Editor(s)

J. Cussens And A. Russo

Conference

26th International Conference On Inductive Logic Programming

Comments

The presentation slides for this paper can be found on the author's website.
An earlier version of this paper was presented at the Sixth International Workshop On Statistical Relational AI (StarAI 2016), July 11, 2016, in New York, NY. The full text of this version is freely available on arXiv.org at arXiv:1607.00424.