Modeling annotator behaviors for crowd labeling

Abstract

Machine learning applications can benefit greatly from vast amounts of data, provided that reliable labels are available. Mobilizing crowds to annotate the unlabeled data is a common solution. Although the labels provided by the crowd are subjective and noisy, the wisdom of crowds can be captured by a variety of techniques. Finding the mean or the median of… (More)
DOI: 10.1016/j.neucom.2014.10.082

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