Mining Documents for Complex Semantic Relations by the Use of Context Classification

  • Andreas Schmidt
  • Markus Junker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2423)

Abstract

Causal relations symbolize one of the most important document organization and knowledge representation principles. Consequently, the identification of cause-effect chains for later evaluation represents a valuable document analysis task. This work introduces a prototype implementation of a causal relation management and evaluation system which functions as a framework for mining documents for causal relations. The central part describes a new approach of classifying passages of documents as relevant considering the causal relations under inspection. The “Context Classification by Distance- Weighted Relevance Feedback” method combines passage retrieval and relevance feedback techniques and extends both of them with regard to the local contextual nature of causal relations. A wide range of parameter settings is evaluated in various experiments and the results are discussed on the basis of recall-precision figures. It is shown that the trained context classifier represents a good means for identifying relevant passages not only for already seen causal relations but also for new ones.

Keywords

Interest Rate Causal Relation Relevance Feedback Retrieval Model Context Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Andreas Schmidt
    • 1
  • Markus Junker
    • 1
  1. 1.German Research Center for Artificial Intelligence (DFKI)KaiserslauternGermany

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