Learning Relational Dependency Networks for Relation Extraction

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 sev...

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Bibliographic Details
Published inarXiv.org
Main Authors Viswanathan, Dileep, Soni, Ameet, Shavlik, Jude, Natarajan, Sriraam
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.07.2016
Subjects
Dependence
Feature extraction
Knowledge base
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Summary: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.
ISSN:2331-8422