Paper: Class Label Enhancement via Related Instances

ACL ID D11-1011
Title Class Label Enhancement via Related Instances
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011

Class-instance label propagation algorithms have been successfully used to fuse informa- tion from multiple sources in order to enrich a set of unlabeled instances with class labels. Yet, nobody has explored the relationships be- tween the instances themselves to enhance an initial set of class-instance pairs. We pro- pose two graph-theoretic methods (centrality and regularization), which start with a small set of labeled class-instance pairs and use the instance-instance network to extend the class labels to all instances in the network. We carry out a comparative study with state-of-the-art knowledge harvesting algorithm and show that our approach can learn additional class labels while maintaining high accuracy. We conduct a comparative study between class-instance and instance-instance...