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<Paper uid="W98-1107">
  <Title>Semantic Lexicon Acquisition for Learning Natural Language Interfaces</Title>
  <Section position="9" start_page="70" end_page="70" type="concl">
    <SectionTitle>
8 Conclusions
</SectionTitle>
    <Paragraph position="0"> Acquiring a semantic lexicon from a corpus of sentences labeled with representations of their meaning is an important problem that has not been widely studied.</Paragraph>
    <Paragraph position="1"> WOLFIE demonstrates that a fairly simple greedy symbolic learning algorithm performs fairly well on this task and obtains performance superior to a previous lexicon acquisition system on a corpus of geography queries. Our results also demonstrate that our methods extend to a variety of natural languages besides English.</Paragraph>
    <Paragraph position="2"> Most experiments in corpus-based natural language have presented results on some subtask of natural language, and there are few results on whether the learned subsystems can be successfully integrated to build a complete NLP system. The experiments presented in this paper demonstrated how two learning systems. WOLFIE and CHILL were successfully integrated to learn a complete NLP system for parsing database queries into executable logical form given only a single corpus of annotated queries.</Paragraph>
  </Section>
class="xml-element"></Paper>