Using language modeling to select useful annotation data
Dmitriy Dligach, Martha Stone Palmer · 2009
An annotation project typically has an abundant supply of unlabeled data that can be drawn from some corpus, but because the labeling process is expensive, it is helpful to pre-screen the pool of the candidate instances based on some criterion of future usefulness. In many cases, that criterion is to improve the presence of the rare classes in the data to be annotated. We propose a novel method for solving this problem and show that it compares favorably to a random sampling baseline and a clustering algorithm.