Graph-based semi-supervised learning (SSL) algorithms have been successfully used to extract class-instance pairs from large unstructured and structured text collections. However, a careful comparison of different graph-based SSL algorithms on that task has been lacking. We compare three graph-based SSL algorithms for class-instance acquisition on a variety of graphs constructed from different domains. 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.
REF conducted a comparative study of graph algorithms and showed that class-instance extraction can be improved using additional information that can be modeled as instance-attribute edges.
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
ACL
Computer Science