document: Cross-training : learning probabilistic mappings between topics Classification is a well-established operation in text mining . Given a set of labels A and a set DA of training documents tagged with these labels , a classifier learns to assign labels to unlabeled test documents . Suppose we also had available a different set of labels B , together with a set of documents DB marked with labels from B. If A and B have some semantic overlap , can the availability of DB help us build a better classifier for A , and vice versa ? We answer this question in the affirmative by proposing cross-training : a new approach to semi-supervised learning in presence of multiple label sets . We give distributional and discriminative algorithms for cross-training and show , through extensive experiments , that cross-training can discover and exploit probabilistic relations between two taxonomies for more accurate classification .
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