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Future work will first focus on conducting new experiments to test the value of the crowd for further different types of quality problems as well as for different LD sets from other knowledge domains. In the longer term, we will also concern ourselves with the question of how to optimally integrate crowd contributions – by implementing the Fix stage – into curation processes and tools, in particular with respect to the trade-offs of costs and quality between manual and automatic ap- proaches. Another area of research is the integration of baseline approaches before the crowdsourcing step in order to filter out errors that can be detected automatically to further increase the productivity of both LD experts and crowd workers.
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