Open Language Learning for Information Extraction

@inproceedings{Mausam2012OpenLL,
  title={Open Language Learning for Information Extraction},
  author={Mausam and Michael Schmitz and Stephen Soderland and Robert Bart and Oren Etzioni},
  booktitle={EMNLP-CoNLL},
  year={2012}
}
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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