Cross training and its application to skill mining

We present an approach for cataloging an organization's skill assets based on electronic communications. Our approach trains classifiers using messages from skill-related discussion groups and then applies those classifiers to a different distribution of person-related e-mail messages. We present a general framework, called cross training, for addressing such discrepancies between the training and test distributions. We outline two instances of the general cross-training problem, develop algorithms for each, and empirically demonstrate the efficacy of our solution in the skill-mining context.