Semi-automatic entity set refinement

Abstract

State of the art set expansion algorithms produce varying quality expansions for different entity types. Even for the highest quality expansions, errors still occur and manual refinements are necessary for most practical uses. In this paper, we propose algorithms to aide this refinement process, greatly reducing the amount of manual labor required. The methods rely on the fact that most expansion errors are systematic, often stemming from the fact that some seed elements are ambiguous. Using our methods, empirical evidence shows that average R-precision over random entity sets improves by 26% to 51% when given from 5 to 10 manually tagged errors. Both proposed refinement models have linear time complexity in set size allowing for practical online use in set expansion systems. © 2009 Association for Computational Linguistics.