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.

• Type: Journal
• Year: 2010
• Where: ACL
• Authors: Raphael Hoffmann, Congle Zhang, Daniel S. Weld
• Keywords: ACL 2010, Average F1 Score, Computational Linguistics, Large-scale Knowledge Bases, Natural Language Text