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<Paper uid="W06-0206">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Data Selection in Semi-supervised Learning for Name Tagging</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present two semi-supervised learning techniques to improve a state-of-the-art multi-lingual name tagger. For English and Chinese, the overall system obtains 1.7% - 2.1% improvement in F-measure, representing a 13.5% - 17.4% relative reduction in the spurious, missing, and incorrect tags. We also conclude that simply relying upon large corpora is not in itself sufficient: we must pay attention to unlabeled data selection too. We describe effective measures to automatically select documents and sentences.</Paragraph>
  </Section>
class="xml-element"></Paper>