Example Labeling Difficulty within Repeated Labeling
نویسنده
چکیده
This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. We show that the repeated-labeling strategies proposed acquire more labels for difficult examples automatically. Among these strategies, the integrated one, combining the uncertainty of the multiple label with the uncertainty predicted from classification models, performs better than the strategies based on the two uncertainties respectively.
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