Learning 5000 Relational Extractors


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 any previous approach — with an average F1 score of 61%. Crucial to LUCHS’s performance is an automated system for dynamic lexicon learning, which allows it to learn accurately from heuristically-generated training data, which is often noisy and sparse.

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@inproceedings{Hoffmann2010Learning5R, title={Learning 5000 Relational Extractors}, author={Raphael Hoffmann and Congle Zhang and Daniel S. Weld}, booktitle={ACL}, year={2010} }