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<Paper uid="W06-3305">
  <Title>A Priority Model for Named Entities</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We introduce a new approach to named entity classification which we term a Priority Model. We also describe the construction of a semantic database called SemCat consisting of a large number of semantically categorized names relevant to biomedicine. We used SemCat as training data to investigate name classification techniques. We generated a statistical language model and probabilistic context-free grammars for gene and protein name classification, and compared the results with the new model. For all three methods, we used a variable order Markov model to predict the nature of strings not represented in the training data. The Priority Model achieves an F-measure of 0.958-0.960, consistently higher than the statistical language model and probabilistic context-free grammar.</Paragraph>
  </Section>
class="xml-element"></Paper>