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<Paper uid="W99-0612">
  <Title>Language Independent Named Entity Recognition Combining Morphological and Contextual Evidence</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Identifying and classifying personal, geographic, institutional or other names in a text is an important task for numerous applications. This paper describes and evaluates a language-independent bootstrapping algorithm based on iterative learning and re-estimation of contextual and mOrphological patterns captured in hierarchically smoothed trie models. The algorithm learns from unannotated text and achieves competitive performance when trained on a very short labelled name list with no other required language-specific information, tokenizers or tools.</Paragraph>
  </Section>
class="xml-element"></Paper>