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Predicting worker disagreement for more effective crowd labeling

, , , and . 2018 IEEE 5th International Conference on Data Science andAdvanced Analytics (DSAA), page 179--188. IEEE, (2018)

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Predicting Worker Disagreement for More Effective Crowd Labeling., , , and . DSAA, page 179-188. IEEE, (2018)A framework for validating the merit of properties that predict the influence of a twitter user, and . Expert Systems with Applications, 42 (5): 2824-2834 (2015)SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification., , , , and . SemEval@NAACL-HLT, page 64-70. The Association for Computer Linguistics, (2016)Discovering the Prerequisite Relationships Among Instructional Videos From Subtitles., , and . EDM, International Educational Data Mining Society, (2020)Predicting worker disagreement for more effective crowd labeling, , , and . 2018 IEEE 5th International Conference on Data Science andAdvanced Analytics (DSAA), page 179--188. IEEE, (2018)Context-based extraction of concepts from unstructured textual documents., , and . Inf. Sci., (2022)Analyzing crowd workers learning behavior to obtain more reliable labels (Kitle çalışanlarının öğrenme tutumlarının daha güvenilir etiketler elde etmek içinanaliz edilmesi). Sabancı University, Turkey, (2018)How do annotators label short texts? Toward understanding the temporal dynamics of tweet labeling, , and . Information Sciences, (2018)