Improving semi-supervised acquisition of relation extraction patterns
Published 1 January 2006Open access
Mark Greenwood, Mark Stevenson
Citations50
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TL;DR
A novel approach to the semi-supervised learning of Information Extraction patterns that makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods.
Abstract
This paper presents a novel approach to the semi-supervised learning of Information Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods (Culotta and Sorensen, 2004). Experiments show that the proposed similarity measure outperforms a previously reported measure based on cosine similarity when used to perform binary relation extraction.
Keywords
Computer Science
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