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<Paper uid="W02-0306">
  <Title>A Transformational-based Learner for Dependency Grammars in Discharge Summaries</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Natural Language is a vital medium in medicine. Health care providers rely on medical narratives for recording, representing and sharing complex medical information such as the description of images, explanation of test results, or the summary of a patient's hospital visit. Natural Language Processing (NLP) tools have been applied to medical narrative for a variety of applications, such as triggering clinical alerts (Friedman, 1997) and document classification (Wilcox, 2000).</Paragraph>
    <Paragraph position="1"> The effort required to create and maintain NLP systems in the medical setting can be prohibitive. Most language processors require a domain-specific semantic lexicon to function and, so far, these lexica have been created manually. The time and cost involved in creating these knowledge structures put limits on the extensibility and portability of NLP systems (Hripcsak, 1998). One solution to this bottleneck is to use machine learning to assist in categorizing lexemes into semantic classes.</Paragraph>
    <Paragraph position="2"> Such a tool could reduce the difficulty in porting NLP systems from one domain to another.</Paragraph>
  </Section>
class="xml-element"></Paper>