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2011

Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

8 years 3 months ago
Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
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 multiinstance learning algorithms to combat the noisy training data that can come from heuristic labeling, but their models assume relations are disjoint — for example they cannot extract the pair Founded(Jobs, Apple) and CEO-of(Jobs, Apple). This paper presents a novel approach for multi-instance learning with overlapping relations that combines a sentence-level extraction model with a simple, corpus-level component for aggregating the individual facts. We apply our model to learn extractors for NY Times text using weak supervision from Freebase. Experiments show that the approach runs quickly and yields sur...
Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke S.
Added 23 Aug 2011
Updated 23 Aug 2011
Type Journal
Year 2011
Where ACL
Authors Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke S. Zettlemoyer, Daniel S. Weld
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