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2010

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

8 years 11 months ago
Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition
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.
Partha Pratim Talukdar, Fernando Pereira
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Partha Pratim Talukdar, Fernando Pereira
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