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<Paper uid="W02-2019">
  <Title>Markov models for language-independent named entity recognition</Title>
  <Section position="4" start_page="0" end_page="0" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> The models described here are very simple and efficient, depend on no preprocessing or (with the exception of a73a77a76a6a86a88a87a91a90 ) external databases, and yet provide a dramatic improvement over a baseline model.</Paragraph>
    <Paragraph position="1"> However, the performance is still quite a bit lower than results for industrial-strength language-specific named entity recognition systems.</Paragraph>
    <Paragraph position="2"> There are a number of small improvements which could be made to these models, such as feature selection (to reduce overtraining) and the use of whole sentence sequence models, as in Lafferty et al. (2001) (to avoid the 'label-bias problem'). These refinements can be expected to offer a modest boost to the performance of the best model.</Paragraph>
  </Section>
class="xml-element"></Paper>