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Expertise of annotators has a major role in crowdsourcing based opinionaggregation models. In such frameworks, accuracy and biasness of annotators areoccasionally taken as important features and based on them priority of theannotators are assigned. But instead of relying on a single feature, multiplefeatures can be considered and separate rankings can be produced to judge theannotators properly. Finally, the aggregation of those rankings with perfectweightage can be done with an aim to produce better ground truth prediction.Here, we propose a novel weighted rank aggregation method and its efficacy withrespect to other existing approaches is shown on artificial dataset. Theeffectiveness of weighted rank aggregation to enhance quality prediction isalso shown by applying it on an Amazon Mechanical Turk (AMT) dataset.

Quality Enhancement by Weighted Rank Aggregation of Crowd Opinion