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<Paper uid="C02-1088">
  <Title>Unsupervised Named Entity Classification Models and their Ensembles</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large hand-tagged training corpus or a lot of rules. Our model uses the ensemble of three different learning methods and repeats the learning with new training examples generated through the ensemble learning.</Paragraph>
    <Paragraph position="1"> The ensemble of various learning methods brings a better result than each individual learning method. The experimental result shows 73.16% in precision and 72.98% in recall for Korean news articles.</Paragraph>
  </Section>
class="xml-element"></Paper>