An extension of the aspect PLSA model to active and semi-supervised learning for text classification

  • Authors:
  • Anastasia Krithara;Massih-Reza Amini;Cyril Goutte;Jean-Michel Renders

  • Affiliations:
  • National Center for Scientific Research (NCSR) 'Demokritos', Athens, Greece;National Research Council Canada, Gatineau, Canada;National Research Council Canada, Gatineau, Canada;Xerox Research Centre Europe, Grenoble, France

  • Venue:
  • SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
  • Year:
  • 2010

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Abstract

In this paper, we address the problem of learning aspect models with partially labeled examples We propose a method which benefits from both semi-supervised and active learning frameworks In particular, we combine a semi-supervised extension of the PLSA algorithm [11] with two active learning techniques We perform experiments over four different datasets and show the effectiveness of the combination of the two frameworks.