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Summary: Knowledge-Based Weak Supervision for Information Extraction
of Overlapping Relations
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, Daniel S. Weld
Computer Science & Engineering
University of Washington
Seattle, WA 98195, USA
{raphaelh,clzhang,xiaoling,lsz,weld}@cs.washington.edu
Abstract
Information extraction (IE) holds the promise
of generating a large-scale knowledge
base from the Web's natural language text.
Knowledge-based weak supervision, using
structured data to heuristically label a training
corpus, works towards this goal by enabling
the automated learning of a potentially
unbounded number of relation extractors.
Recently, researchers have developed multi-
instance learning algorithms to combat the
noisy training data that can come from
heuristic labeling, but their models assume
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