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<Paper uid="A00-1036">
  <Title>Linguistic Knowledge can Improve Information Retrieval</Title>
  <Section position="8" start_page="265" end_page="265" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have described some experiments using linguistic knowledge in an information retrieval system in which passages within texts are dynamically found in response to a query and are scored and ranked based on a relaxation of constraints.</Paragraph>
    <Paragraph position="1"> This is a different approach from previous methods of passage retrieval and from previous attempts to use linguistic knowledge in information retrieval.</Paragraph>
    <Paragraph position="2"> These experiments show that linguistic knowledge can significantly improve information retrieval performance when incorporated into a knowledge-based relaxation-ranking algorithm for specific passage retrieval. null The linguistic knowledge considered here includes the use of morphological relationships between words, taxonomic relationships between concepts, and general semantic entailment relationships between words and concepts. We have shown that the combination of these three knowledge sources can significantly improve performance in finding appropriate answers to specific queries when incorporated into a relaxation-ranking algorithm. It appears that the penalty-based relaxation-ranking algorithm figures crucially in this success, since the addition of such linguistic knowledge to traditional information retrieval models typically degrades retrieval performance rather than improving it, a pattern that was borne out in our own experiments.</Paragraph>
  </Section>
class="xml-element"></Paper>