[PDF][PDF] Learning 5000 relational extractors
Proceedings of the 48th Annual Meeting of the Association for …, 2010•aclanthology.org
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 …
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 …
Abstract
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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