<?xml version="1.0" standalone="yes"?>
<Paper uid="W02-0305">
  <Title>MPLUS: A Probabilistic Medical Language Understanding System</Title>
  <Section position="5" start_page="5" end_page="5" type="concl">
    <SectionTitle>
Conclusions
</SectionTitle>
    <Paragraph position="0"> M+ and its predecessors have demonstrated that BNs provide a useful semantic model for medical text processing. In practice, a medical NLP system will frequently encounter missing and unknown words, unknown and ungrammatical phrase structures, and telegraphic usages. Knowledge databases will be imperfect and incomplete. Using BNs for semantic representation brings a noise-tolerant, partial match-tolerant, context-sensitive character to the recognition of semantic patterns, and to relevant inferences based on those patterns. In addition, BNs can be used to guess the semantic types of unknown words, providing a basis for bootstrapping the system's semantic knowledge.</Paragraph>
  </Section>
class="xml-element"></Paper>