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 functions that sort the original training examples in terms of how likely it is that the human annotator has mislabelled them. This provides a convenient means for the human annotator to revise the training set so as to improve its quality. Working in the context of boosting-based learning methods for multilabel classification we present three different techniques for performing TLC and, on three widely used TC benchmarks, evaluate them by their capability of spotting training documents that, for experimental reasons only, we have purposefully mislabelled. We also evaluate the degradation in classification effectiveness that these mislabelled texts bring about, and to what extent training label cleaning can prevent this degradation.