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<Paper uid="C04-1098">
  <Title>A Trigger Language Model-based IR System</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Language model based IR system proposed in recent 5 years has introduced the language model approach in the speech recognition area into the IR community and improves the performance of the IR system effectively.</Paragraph>
    <Paragraph position="1"> However, the assumption that all the indexed words are irrelative behind the method is not the truth. Though statistical MT approach alleviates the situation by taking the synonymy factor into account, it never helps to judge the different meanings of the same word in varied context. In this paper we propose the trigger language model based IR system to resolve the problem. Firstly we compute the mutual information of the words from training corpus and then design the algorithm to get the triggered words of the query in order to fix down the topic of query more clearly. We introduce the relative parameters into the document language model to form the trigger language model based IR system. Experiments show that the performance of trigger language model based IR system has been improved greatly. The precision of trigger language model increased 12% and recall increased nearly 10.8% compared with Ponte language model method.</Paragraph>
  </Section>
class="xml-element"></Paper>