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<Paper uid="W06-3319">
  <Title>Biomedical Term Recognition With the Perceptron HMM Algorithm</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We propose a novel approach to the identi cation of biomedical terms in research publications using the Perceptron HMM algorithm. Each important term is identi ed and classi ed into a biomedical concept class. Our proposed system achieves a 68.6% F-measure based on 2,000 training Medline abstracts and 404 unseen testing Medline abstracts. The system achieves performance that is close to the state-of-the-art using only a small feature set. The Perceptron HMM algorithm provides an easy way to incorporate many potentially interdependent features.</Paragraph>
  </Section>
class="xml-element"></Paper>