Learning 5000 relational extractors

Learning 5000 relational extractors (2010)

by Raphael Hoffmann , Congle Zhang , Daniel S. Weld

Venue:	In ACL
Citations:	9 - 1 self

Datum	Value	Source
TITLE	Learning 5000 relational extractors	INFERENCE
AUTHOR NAME	Raphael Hoffmann	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA-98195, USA	SVM HeaderParse 0.2
AUTHOR NAME	Congle Zhang	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA-98195, USA	SVM HeaderParse 0.2
AUTHOR NAME	Daniel S. Weld	SVM HeaderParse 0.2
AUTHOR AFFIL	Computer Science & Engineering; University of Washington	SVM HeaderParse 0.2
AUTHOR ADDR	Seattle, WA-98195, USA	SVM HeaderParse 0.2
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. 1	SVM HeaderParse 0.2
YEAR	2010	INFERENCE
VENUE	In ACL	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	34 found	ParsCit 1.0