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Crowdsourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their method of motivating subjects to contribute and the scale of their applications. To date, however, there has yet to be a study that helps the practitioner to decide what form an annotation application should take to best reach its objectives within the constraints of a project. The authors first provide a faceted analysis of existing crowdsourcing annotation applications.
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