Learning 5000 Relational Extractors

@inproceedings{Hoffmann2010Learning5R,
  title={Learning 5000 Relational Extractors},
  author={Raphael Hoffmann and Congle Zhang and Daniel S. Weld},
  booktitle={ACL},
  year={2010}
}
Many researchers are trying to use information extraction (IE) to create large-scale knowledge bases from natural language text on the Web. However, the primary approach (supervised learning of relation-specific extractors) requires manually-labeled training data for each relation and doesn’t scale to the thousands of relations encoded in Web text. This paper presents LUCHS, a self-supervised, relation-specific IE system which learns 5025 relations — more than an order of magnitude greater than… CONTINUE READING

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  • This paper presents LUCHS, a self-supervised, relation-specific IE system which learns 5025 relations — more than an order of magnitude greater than any previous approach — with an average F1 score of 61%.

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