Invited Talk: Cost-Effective Data Annotation using Game-Based Crowdsourcing Slides

Jingru Yang, hosted by Xueran Han

Large-scale data annotation is indispensable for many applications, such as machine learning and data integration. However, existing annotation solutions either incur expensive cost for large datasets or produce noisy results. This talk introduces a cost-effective annotation approach, and focuses on the labeling rule generation problem that aims to generate high-quality rules to largely reduce the labeling cost while preserving quality. This work is done by Jingru Yang and published on VLDB’19.

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