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)


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.


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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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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