Paper: Unsupervised Relation Extraction with General Domain Knowledge

ACL ID D13-1040
Title Unsupervised Relation Extraction with General Domain Knowledge
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

In this paper we present an unsupervised ap- proach to relational information extraction. Our model partitions tuples representing an observed syntactic relationship between two named entities (e.g., ?X was born in Y? and ?X is from Y?) into clusters correspond- ing to underlying semantic relation types (e.g., BornIn, Located). Our approach incor- porates general domain knowledge which we encode as First Order Logic rules and auto- matically combine with a topic model devel- oped specifically for the relation extraction task. Evaluation results on the ACE 2007 English Relation Detection and Categoriza- tion (RDC) task show that our model outper- forms competitive unsupervised approaches by a wide margin and is able to produce clus- ters shaped by both the data and the rules.