Improves prediction accuracy in document classification by measuring uncertainty.
problem Ensuring limited human resources focus on uncertain predictions in text classification.
method Proposes a neural-network-based model using dropout-entropy for uncertainty measurement and metric learning on feature representations.
result Significant improvement in overall prediction accuracy, from 0.78 to 0.92, when 30% of most uncertain predictions are handed over to human experts.