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<Paper uid="W04-1219">
  <Title>Smoothing Technniques for Language</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper, we present a named entity recognition system in the biomedical domain. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a Hidden Markov Model (HMM). In addition, a Support Vector Machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in our system. Besides the widely used lexical-level features, such as word formation pattern, morphological pattern, out-domain POS and semantic trigger, we also explore the name alias phenomenon, the cascaded entity name phenomenon, the use of both a closed dictionary from the training corpus and an open dictionary from the database term list SwissProt and the alias list LocusLink, the abbreviation resolution and in-domain POS using the GENIA corpus.</Paragraph>
  </Section>
class="xml-element"></Paper>