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<Paper uid="W03-0431">
  <Title>Meta-Learning Orthographic and Contextual Models for Language Independent Named Entity Recognition</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> There are commonly considered to be two main tasks in named entity recognition, recognition (NER) and classification (NEC). As the features that best classify words according to the two tasks are somewhat disparate, the two are often separated. Attribute sets may be further divided into subsets through sub-grouping of attributes, sub-grouping of instances and/or the use of multiple classifying processes. While the use of multiple subsets can increase overall accuracy, the recombination of models has been shown to propagate errors (Carreras et al., 2002; Patrick et al., 2002). More importantly, the decision regarding the separation of attributes into various subsets is often a manual task. As it is reasonable to assume that the same attributes will have different relative levels of significance in different languages, using the same division of attributes across languages will be less than optimal, while a manual redistribution across different languages is limited by the users knowledge of those languages. In this paper, the division and subsequent recombination of subgroups is treated as a meta-learning task.</Paragraph>
  </Section>
class="xml-element"></Paper>