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<Paper uid="I05-2023">
  <Title>Improved-Edit-Distance Kernel for Chinese Relation Extraction</Title>
  <Section position="10" start_page="136" end_page="136" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We presented a new approach for using kernel-based machine learning methods for extracting relations between named entities from Chinese text sources. We define kernels over the original representations of Chinese strings around the particular entities and use the IED method for computing the kernel function. The kernel-based methods need not transform the original expression of objects into feature vectors, so the methods need less manual efforts than the feature-based methods. We applied the Voted Perceptron and the SVM learning method with custom kernels to extract the person-affiliation relations. The method can be extended to extract other relations between entities, such as organization-location, etc. We also compared the performance of kernel-based methods with that of feature-based methods, and the experimental results show that kernel-based methods are better than feature-based methods.</Paragraph>
  </Section>
class="xml-element"></Paper>