Learning Relational Dependency Networks For Relation Extraction
Inductive Logic Programming
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.
J. Cussens And A. Russo
26th International Conference On Inductive Logic Programming
Ameet Soni, D. Viswanathan, J. Shavlik, and S. Natarajan.
"Learning Relational Dependency Networks For Relation Extraction".
Inductive Logic Programming.