Learning Relational Dependency Networks for Relation Extraction
This work addresses relation extraction for knowledge base construction, but it is incremental as it builds on existing relational frameworks with added components.
The authors tackled the problem of extracting relation information from newswire documents for knowledge base construction using Relational Dependency Networks (RDNs), achieving competitive performance with state-of-the-art methods in the KBP 2015 benchmark.
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.