Kernel Methods for Relation Extraction
Dmitry Zelenko, Chinatsu Aone, Anthony Richardella;
3(Feb):1083-1106, 2003.
Abstract
We present an application of kernel methods to extracting
relations from unstructured natural language sources.
We introduce kernels defined over shallow parse representations of text, and
design efficient algorithms for computing the kernels. We
use the devised kernels in conjunction with Support Vector
Machine and Voted Perceptron learning algorithms for the
task of extracting
person-affiliation and
organization-location relations from text. We
experimentally evaluate the proposed methods and compare
them with feature-based learning algorithms, with promising
results.
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