Learning cross-document structural relationships using boosting

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning; Induction
      I.2.7 Natural Language Processing
          Subjects: Discourse

General Terms:
Algorithms, Experimentation

Keywords:
boosting, classification, cross-document structure, discourse analysis