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Predicting worker disagreement for more effective crowd labeling

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

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