Paper: Semi-Automatic Entity Set Refinement

ACL ID N09-1033
Title Semi-Automatic Entity Set Refinement
Venue Human Language Technologies
Session Main Conference
Year 2009

State of the art set expansion algorithms pro- duce varying quality expansions for different entity types. Even for the highest quality ex- pansions, errors still occur and manual re- finements 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 me- thods rely on the fact that most expansion er- rors 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...