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Improving Text Classification Accuracy by Training Label Cleaning
2013
ACM Transactions on Information Systems
In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain. Semisupervised learning and active learning are two strategies whose aim is maximizing the effectiveness of the resulting classifiers for a given amount of training effort. Both strategies have been actively investigated for TC in recent years. Much less research has been devoted to a third such strategy, training label cleaning (TLC), which consists in devising ranking
doi:10.1145/2516889
fatcat:alkr7t4h4jb2hj5er2uycfftti