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Self-supervised Relation Extraction from the Web

  • Ronen Feldman
  • Benjamin Rosenfled
  • Stephen Soderland
  • Oren Etzioni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. SRES is a self-supervised Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target elations and their attributes. SRES automatically generates the training data needed for its pattern-learning component. We also compare the performance of SRES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than SRES.

Keywords

Noun Phrase Information Extraction True Recall Negative Sentence Relation Instance 
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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References

  1. 1.
    Cardie, C.: Empirical Methods in Information Extraction. AI Magazine 18(4), 65–80 (1997)Google Scholar
  2. 2.
    Cowie, J., Lehnert, W.: Information Extraction. Communications of the Association of Computing Machinery 39(1), 80–91 (1996)Google Scholar
  3. 3.
    Freitag, D., McCallum, A.: Information Extraction with HMM Structures Learned by Stochastic Optimization. In: AAAI/IAAI, pp. 584–589 (2000)Google Scholar
  4. 4.
    Etzioni, O., et al.: Unsupervised named-entity extraction from the Web: An experimental study. Artificial Intelligence 165(1), 91–134 (2005)CrossRefGoogle Scholar
  5. 5.
    Riloff, E., Jones, R.: Learning Dictionaries for Information Extraction by Multi-level Boot-strapping. In: AAAI 1999 (1999)Google Scholar
  6. 6.
    Brin, S.: Extracting Patterns and Relations from the World Wide Web. In: WebDB Workshop at 6th International Conference on Extending Database Technology, EDBT 1998, Valencia, Spain (1998)Google Scholar
  7. 7.
    Agichtein, E., Gravano, L.: Snowball: Extracting Relations from Large Plain-Text Collections. In: Proceedings of the 5th ACM International Conference on Digital Libraries (DL) (2000)Google Scholar
  8. 8.
    Ravichandran, D., Hovy, E.: Learning Surface Text Patterns for a Question Answering System. In: 40th ACL Conference (2002)Google Scholar
  9. 9.
    Ciravegna, F.: Adaptive Information Extraction from Text by Rule Induction and Generalization. In: Proceedings of the 17th IJCAI 2001, Seattle, WA (2001)Google Scholar
  10. 10.
    Soderland, S.: Learning Information Extraction Rules for Semi-Structured and Free Text. Machine Learning 34(1-3), 233–272 (1999)MATHCrossRefGoogle Scholar
  11. 11.
    Hasegawa, T., Sekine, S., Grishman, R.: Discovering Relations among Named Entities from Large Corpora. In: ACL 2004 (2004)Google Scholar
  12. 12.
    Miller, G.: WordNet: An on-line lexical database. International Journal of Lexicography 3(4), 235–312 (1990)CrossRefGoogle Scholar
  13. 13.
    Genkin, A., Lewis, D.D., Madigan, D.: Large-Scale Bayesian Logistic Regression for Text Categorization, pp. 1–41. DIMACS, New Brunswick (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ronen Feldman
    • 1
  • Benjamin Rosenfled
    • 1
  • Stephen Soderland
    • 2
  • Oren Etzioni
    • 2
  1. 1.Computer Science DepartmentBar-Ilan UniversityRamat GanIsrael
  2. 2.Department of Computer ScienceWashington UniversitySeattleUSA

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