Cross training and its application to skill mining

Abstract

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.