Meta-level Information Extraction

  • Peter Kluegl
  • Martin Atzmueller
  • Frank Puppe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5803)

Abstract

This paper presents a novel approach for meta-level information extraction (IE). The common IE process model is extended by utilizing transfer knowledge and meta-features that are created according to already extracted information. We present two real-world case studies demonstrating the applicability and benefit of the approach and directly show how the proposed method improves the accuracy of the applied information extraction technique.

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References

  1. 1.
    Ferrucci, D., Lally, A.: UIMA: An Architectural Approach to Unstructured Information Processing in the Corporate Research Environment. Nat. Lang. Eng. 10(3-4), 327–348 (2004)CrossRefGoogle Scholar
  2. 2.
    McCallum, A., Li, W.: Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons. In: Proc. of the seventh conference on Natural language learning at HLT-NAACL 2003, pp. 188–191. Association for Computational Linguistics, Morristown (2003)CrossRefGoogle Scholar
  3. 3.
    Turmo, J., Ageno, A., Català, N.: Adaptive Information Extraction. ACM Comput. Surv. 38(2), 4 (2006)CrossRefGoogle Scholar
  4. 4.
    Li, D., Savova, G., Kipper-Schuler, K.: Conditional Random Fields and Support Vector Machines for Disorder Named Entity Recognition in Clinical Texts. In: Proc. of the Workshop on Current Trends in Biomedical Natural Language Processing, pp. 94–95. Association for Computational Linguistics, Columbus (2008)CrossRefGoogle Scholar
  5. 5.
    Ogren, P.V., Wetzler, P.G., Bethard, S.: ClearTK: A UIMA Toolkit for Statistical Natural Language Processing. In: UIMA for NLP workshop at Language Resources and Evaluation Conference, LREC (2008)Google Scholar
  6. 6.
    Atzmueller, M., Kluegl, P., Puppe, F.: Rule-Based Information Extraction for Structured Data Acquisition using TextMarker. In: Proc. of the LWA 2008 (KDML Track), pp. 1–7 (2008)Google Scholar
  7. 7.
    Kluegl, P., Atzmueller, M., Puppe, F.: Test-Driven Development of Complex Information Extraction Systems using TextMarker. In: KESE at KI 2008 (2008)Google Scholar
  8. 8.
    Cunningham, H., Maynard, D., Tablan, V.: JAPE: A Java Annotation Patterns Engine, 2 edn. Research Memorandum CS–00–10, Department of Computer Science, University of Sheffield (November 2000)Google Scholar
  9. 9.
    Flach, P.A., Lavrac, N.: The Role of Feature Construction in Inductive Rule Learning. In: Raedt, L.D., Kramer, S. (eds.) Proc. ICML 2000 Workshop on Attribute-Value and Relational Learning: crossing the boundaries, 17th International Conference on Machine Learning, July 2000, pp. 1–11 (2000)Google Scholar
  10. 10.
    Sigletos, G., Paliouras, G., Spyropoulos, C.D., Stamatopoulos, T.: Meta-Learning beyond Classification: A Framework for Information Extraction from the Web. In: Proc. of the Workshop on Adaptive Text Extraction and Mining. The 14th Euro. Conf. on Machine Learning and the 7th Euro. Conf. on Principles and Practce of knowledge Discovery in Databases (2003)Google Scholar
  11. 11.
    Thomas, B.: Machine Learning of Information Extraction Procedures - An ILP Approach. PhD thesis, Universität Koblenz-Landau (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peter Kluegl
    • 1
  • Martin Atzmueller
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer Science VIUniversity of WürzburgWürzburgGermany

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