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<Paper uid="M98-1019">
  <Title>NYU: DESCRIPTION OF THE JAPANESE NE SYSTEM USED FOR MET-2</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> In this paper, experimentsonthe Japanese Named Entitytask are reported. We employed a supervised learningmechanism. Recently,several systems have been proposed for this task, butmanyofthem use hand-coded patterns. Creatingthese patterns is laborious work, andwhen weadapt these systems toanew domain or a new de#0Cnition of named entities, it is likely toneed a large amount of additional work. On the other hand, in a supervised learning system, whatisneeded toadapt the system is tomakenew trainingdata andmaybe additional small work. While this is also not a very easy task, it would be easier than creating complicated patterns. For example, based on our experience, 100 training articles can be created in a day.</Paragraph>
    <Paragraph position="1"> There also have been several machine learning systems applied tothis task. However, these either 1#29 partially need hand-made rules, 2#29 have parameters whichmust be adjusted byhand 3#29 do not perform well by fully automatic means or 4#29 need a huge trainingdata. Our system does not work fully automatically,but performs well withasmall training corpus and does not have parameters to be adjusted byhand. We will discuss oneofthe related systems later..</Paragraph>
  </Section>
class="xml-element"></Paper>