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<Paper uid="W05-0401">
  <Title>A Novel Machine Learning Approach for the Identification of Named Entity Relations</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, a novel machine learning approach for the identification of named entity relations (NERs) called positive and negative case-based learning (PNCBL) is proposed. It pursues the improvement of the identification performance for NERs through simultaneously learning two opposite cases and automatically selecting effective multi-level linguistic features for NERs and non-NERs. This approach has been applied to the identification of domain-specific and cross-sentence NERs for Chinese texts.</Paragraph>
    <Paragraph position="1"> The experimental results have shown that the overall average recall, precision, and F-measure for 14 NERs are 78.50%, 63.92% and 70.46% respectively. In addition, the above F-measure has been enhanced from 63.61% to 70.46% due to adoption of both positive and negative cases.</Paragraph>
  </Section>
class="xml-element"></Paper>