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<Paper uid="M92-1018">
  <Title>SRA SOLOMON : MUC-4 TEST RESULTS AND ANALYSI S</Title>
  <Section position="7" start_page="137" end_page="141" type="concl">
    <SectionTitle>
LESSONS LEARNED AND REAFFIRMED BY MUC- 4
</SectionTitle>
    <Paragraph position="0"> We have learned and reaffirmed the following points as the most crucial aspects of successful text under standing for data extraction .</Paragraph>
    <Paragraph position="1"> Overcoming the Knowledge Acquisition Bottleneck : We must develop techniques and tools for acquiring timely, complete, and proven system data .</Paragraph>
    <Paragraph position="2"> Solving the Parsing Problem : We need more robust, semantically constrained syntactic analysis . Grammars must be broad-coverage and highly accurate on complex input .</Paragraph>
    <Paragraph position="3"> Developing Sophisticated Discourse Analysis : We must handle real world discourse phenomena foun d in actual texts . The discourse architecture must be flexible enough to accommodate particular discours e phenomena which are crucial in particular domains or languages .</Paragraph>
    <Paragraph position="4"> MUC-4 has reaffirmed our knowledge of what is involved in porting an NLP system to a new domain . 9 staff months is a bare minimum for such an effort . Improved knowledge acquisition tools as well a s  on-line resources are desirable. To ensure good results, it is necessary to have sufficient time for knowledg e engineering, testing and evaluation . Our experience underscores the fact that natural language understandin g is a highly data-driven problem . The system's performance is often proportional to the level of understandin g of the input and output . The MUC-4 development texts and templates were extremely helpful in this regard .</Paragraph>
  </Section>
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