Learning cross-document structural relationships using boosting

  • Authors:
  • Zhu Zhang;Jahna Otterbacher;Dragomir Radev

  • Affiliations:
  • University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI;University of Michigan, Ann Arbor, MI

  • Venue:
  • CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
  • Year:
  • 2003

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Abstract

Multi-document discoure analysis has emerged with the potential of improving various information retrieval applications. Based on the newly proposed Cross-document Structure Theory (CST), this paper describes an empirical study that uses boosting to classify CST relationships between sentence pairs extracted from topically related documents. We show that the binary classifier for determining existence of structural relationships significantly outperforms the baseline. We also achieve promising results on the multi-class case in which the full taxonomy of relationships are considered.