  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.
