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<Paper uid="W00-0603">
  <Title>A Rule-based Question Answering System for Reading Comprehension Tests</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> In the United States, we evaluate the reading ability of children by giving them reading comprehension tests. These test typically consist of a short story followed by questions. Presumably, the tests are designed so that the reader must understand important aspects of the story to answer the questions correctly. For this reason, we believe that reading comprehension tests can be a valuable tool to assess the state of the art in natural language understanding.</Paragraph>
    <Paragraph position="1"> These tests are especially challenging because they can discuss virtually any topic. Consequently, broad-coverage natural language processing (NLP) techniques must be used. But the reading comprehension tests also require semantic understanding, which is difficult to achieve with broad-coverage techniques.</Paragraph>
    <Paragraph position="2"> We have developed a system called Quarc that &amp;quot;takes&amp;quot; reading comprehension tests.</Paragraph>
    <Paragraph position="3"> Given a story and a question, Quarc finds the sentence in the story that best answers the question. Quarc does not use deep language understanding or sophisticated techniques, yet it achieved 40% accuracy in our experiments.</Paragraph>
    <Paragraph position="4"> Quarc uses hand-crafted heuristic rules that look for lexical and semantic clues in the question and the story. In the next section, we describe the reading comprehension tests. In the following sections, we describe the rules used by Quarc and present experimental results.</Paragraph>
  </Section>
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