Training Data Cleaning for Text Classification

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Training Data Cleaning for Text Classification
Abstract. In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain; strategies are thus needed for maximizing the effectiveness of the resulting classifiers while minimizing the required amount of training effort. Training data cleaning (TDC) consists in devising ranking functions that sort the original training examples in terms of how likely it is that the human annotator has misclassified them, thereby providing 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 we present three different techniques for performing TDC and, on two widely used TC benchmarks, evaluate them by their capability of spotting misclassified texts purposefully inserted in the training set.
Andrea Esuli, Fabrizio Sebastiani
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Authors Andrea Esuli, Fabrizio Sebastiani
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