Paper: Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition

ACL ID P10-1149
Title Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text col- lections. However, a careful comparison of different graph-based SSL algorithms on that task has been lacking. We com- pare three graph-based SSL algorithms for class-instance acquisition on a variety of graphs constructed from different do- mains. We find that the recently proposed MAD algorithm is the most effective. We also show that class-instance extraction can be significantly improved by adding semantic information in the form of instance-attribute edges derived from an independently developed knowledge base. All of our code and data will be made publicly available to encourage reproducible research in this area.