Learning Relation Extraction Grammars with Minimal Human Intervention: Strategy, Results, Insights and Plans

  • Hans Uszkoreit
Conference paper

DOI: 10.1007/978-3-642-19437-5_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6609)
Cite this paper as:
Uszkoreit H. (2011) Learning Relation Extraction Grammars with Minimal Human Intervention: Strategy, Results, Insights and Plans. In: Gelbukh A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg

Abstract

The paper describes the operation and evolution of a linguistically oriented framework for the minimally supervised learning of relation extraction grammars from textual data. Cornerstones of the approach are the acquisition of extraction rules from parsing results, the utilization of closed-world semantic seeds and a filtering of rules and instances by confidence estimation. By a systematic walk through the major challenges for this approach the obtained results and insights are summarized. Open problems are addressed and strategies for solving these are outlined.

Keywords

relation extraction information extraction minimally supervised learning bootstrapping approaches to IE 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hans Uszkoreit
    • 1
  1. 1.German Research Center for Artificial Intelligence (DFKI)Saarland UniversitySaarbrückenGermany

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