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<Paper uid="N06-2024">
  <Title>NER Systems that Suit User's Preferences: Adjusting the Recall-Precision Trade-off for Entity Extraction</Title>
  <Section position="5" start_page="95" end_page="95" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> We described an approach that is based on modifying an existing learned sequential classifier to change the recall-precision tradeoff, guided by a user-provided performance criterion. This approach not only allows one to explore a recall-precision tradeoff, but actually allows the user to specify a performance metric to optimize, and optimizes a learned NER system for that metric. We showed that using a single free parameter and a Gauss-Newton line search (where the objective is iteratively approximated by quadratics), effectively optimizes two plausible performance measures, token6We varied b over the values 0.2, 0.5, 0.8, 1, 1.2, 1.5, 2, 3 and 5 level Fb and entity-level Fb. This approach is in fact general, as it is applicable for sequential and/or structured learning applications other than NER.</Paragraph>
  </Section>
class="xml-element"></Paper>