Cross training and its application to skill mining

  • Authors:
  • D. A. Oblinger;M. Reid;M. Brodie;R. De Salvo Braz

  • Affiliations:
  • IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York;University of South Wales, Sidney, Australia;IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, New York;University of Illinois, Urbana, Illinois

  • Venue:
  • IBM Systems Journal
  • Year:
  • 2002

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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.