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Crowdsourcing as a preprocessing for complex semantic annotation tasks
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Crowdsourcing as a preprocessing for complex semantic annotation tasks
This article outlines a methodology that uses crowdsourcing to reduce the workload of experts for complex semantic tasks. We split turker-annotated datasets into a high-agreement block, which is not modified, and a low-agreement block, which is re-annotated by experts. The resulting annotations have higher observed agreement. We identify different biases in the annotation for both turkers and experts.
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