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<Paper uid="P79-1002">
  <Title>TOWARDS A SELF-EXTENDING PARSER</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> Natural language analysis, like most other subfields of Artificial Intelligence and Computational Linguistics, suffers from the fact that computer systems are unable to automatically better themselves. Automated learning ia considered a very difficult problem, especially when applied to natural language understanding. Consequently, little effort ha8 been focused on this problem. Some pioneering work in Artificial intelligence, such as AM \[I\] and Winston's learning system 1&amp;quot;2\] strove to learn or discover concept descriptions in well-defined domains. Although their efforts produced interesting Ideas and techniques, these techniques do not fully extend to * domain as complex as natural language analysis.</Paragraph>
    <Paragraph position="1"> Rather than attempting the formidable task of creating a language learning system, I will discuss techniques for Incrementally Increasing the abilities of a flexible language analyzer. There are many tasks that can be considered &amp;quot;Incremental language learning&amp;quot;. Initially the learning domain Is restricted to learning the meaning of new words and generalizing existing word definitions. There ere a number of A.I. techniques, and combinations of these techniques capable of exhibiting incremental learning behavior. I first discuss FOULUP and POLITICS, two programs that exhibit a limited capability for Incremental word learning. Secondly, the technique of semantic constraint projection end Integration, as Implemented in POLITICS, Is analyzed in some detail.</Paragraph>
    <Paragraph position="2"> Finally, I discuss the application of some general learning techniques to the problem of generalizing word definitions end understanding metaphors.</Paragraph>
  </Section>
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