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)


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.


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.


  1. 1.
    P.G. Meyer. The relevance of causality. in: E. Couper-Kuhlen. Cause — Condition-Concession — Contrast: Cognitive and Discourse Perspective. Mouton de Gruyter, Berlin, New-York, 2000, pp. 9–34Google Scholar
  2. 2.
    C. Wenzel, H. Maus. An Approach to Context-driven Document Analysis and Understanding. in: 4th IAPR International Workshop On Document Analysis Systems — DAS’2000, Rio de Janeiro, Brazil, Dec. 2000, pp. 121–133Google Scholar
  3. 3.
    J. Gausemeier, A. Fink, O. Schlake. Szenario-Management: Planen und Führen mit Szenarien, 2. Edition, Hanser Verlag München, 1996Google Scholar
  4. 4.
    R. Baeze-Yates, B. Ribeiro-Neto. Modern Information Retrieval. Addison-Wesley, 1999Google Scholar
  5. 5.
    K. Kise et al. Passage-Based Document Retrieval as a Tool for Text Mining with User’s Information Needs. in: K. P. Jantke, A. Shinohara eds. Discovery Science, 4 th International Conference, DS 2001, Washington. Lecture Notes in Computer Science, Springer-Verlag, 2001, pp. 155–169Google Scholar
  6. 6.
    C. D. Manning, H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge, 2000Google Scholar
  7. 7.
    Y. Ogawa et al. Structuring and Expanding Queries in the Probabilistic Model. in: Proceedings of the 9 th Text Retrieval Conference (TREC-9). NIST Special Publication, 2001Google Scholar
  8. 8.
    M. Adriani, C.J. van Rijsbergen. Informative term selection for automatic query expansion. in: S. Abiteboul, A.-M. Vercoustre eds.. ECDL 1999, Springer-Verlag, Berlin-Heidelberg, pp. 311–322Google Scholar
  9. 9.
    L. Kaelbling, M. Littman. Reinforcement Learning: A Survey. in: Journal of Artificial Intelligence Research 4 (1996), pp. 237–285, Morgan Kaufmann PublishersGoogle Scholar
  10. 10.
    I. Muslea. Extraction Patterns for Information Extraction Tasks: A Survey. in: American Association for Artificial Intelligence, 1999 Google Scholar

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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