Semi-Automatic Entity Set Refinement

  title={Semi-Automatic Entity Set Refinement},
  author={Vishnu Vyas and Patrick Pantel},
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… CONTINUE READING
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Key Quantitative Results

  • 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.

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