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• W2094321623 abstract "This survey deals with the problem of evaluating the submissions to crowdsourcing websites on which data is increasing rapidly in both volume and complexity. Usually expert committees are installed to rate submissions, select winners and adjust monetary rewards. Thus, with an increasing number of submissions, this process is getting more complex, time-consuming and hence expensive. In this paper we suggest following text mining methodology, foremost similarity measurements and clustering algorithms, to evaluate the quality of submissions to crowdsourcing contests semi-automatically. We evaluate our approach by comparing text mining based measurement of more than 40'000 submissions with the real-world decisions made by expert committees using Precision and Recall together with F <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -score." @default.
• W2094321623 created "2016-06-24" @default.
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• W2094321623 date "2013-01-01" @default.
• W2094321623 modified "2023-09-27" @default.
• W2094321623 title "A Text Mining Approach to Evaluate Submissions to Crowdsourcing Contests" @default.
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• W2094321623 doi "https://doi.org/10.1109/hicss.2013.64" @default.
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