Paper: Graph-Based Seed Set Expansion for Relation Extraction Using Random Walk Hitting Times

ACL ID N13-1094
Title Graph-Based Seed Set Expansion for Relation Extraction Using Random Walk Hitting Times
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

Iterative bootstrapping methods are widely employed for relation extraction, especially because they require only a small amount of human supervision. Unfortunately, a phenomenon known as semantic drift can affect the accuracy of iterative bootstrapping and lead to poor extractions. This paper proposes an alternative bootstrapping method, which ranks relation tuples by measuring their distance to the seed tuples in a bipartite tuple-pattern graph. In contrast to previous bootstrapping methods, our method is not susceptible to semantic drift, and it empirically results in better extractions than iterative methods.