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2012

Ontological Smoothing for Relation Extraction with Minimal Supervision

7 years 10 months ago
Ontological Smoothing for Relation Extraction with Minimal Supervision
Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learning to generate extractors from sentences that have been manually labeled with the relations’ arguments. Unfortunately, these methods require numerous training examples, which are expensive and time-consuming to produce. This paper presents ontological smoothing, a semi-supervised technique that learns extractors for a set of minimally-labeled relations. Ontological smoothing has three phases. First, it generates a mapping between the target relations and a background knowledge-base. Second, it uses distant supervision to heuristically generate new training examples for the target relations. Finally, it learns an extractor from a combination of the original and newly-generated examples. Experiments on 65 relations across three target domains show that ontological smoothing can dramatically improve precision ...
Congle Zhang, Raphael Hoffmann, Daniel S. Weld
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where AAAI
Authors Congle Zhang, Raphael Hoffmann, Daniel S. Weld
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