Open Language Learning for Information Extraction

  title={Open Language Learning for Information Extraction},
  author={Mausam and Michael Schmitz and Stephen Soderland and Robert Bart and Oren Etzioni},
Open Information Extraction (IE) systems extract relational tuples from text, without requiring a pre-specified vocabulary, by identifying relation phrases and associated arguments in arbitrary sentences. However, stateof-the-art Open IE systems such as REVERB and WOE share two important weaknesses – (1) they extract only relations that are mediated by verbs, and (2) they ignore context, thus extracting tuples that are not asserted as factual. This paper presents OLLIE, a substantially improved… CONTINUE READING
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  • OLLIE obtains 2.7 times the area under precision-yield curve (AUC) compared to REVERB and 1.9 times the AUC of WOE.


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, and Daniel S . Weld . 2010 . Learning 5000 relational extractors

  • Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke S. Zettlemoyer, Daniel S. Weld
  • ceedings of the 48 th Annual Meeting of the…
  • 2011
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