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<Paper uid="W91-0215">
  <Title>A model for the interaction of lexical and non-lexical knowledge in the determination of word meaning</Title>
  <Section position="3" start_page="0" end_page="165" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> A system with the capability of natural language understanding typically relies on knowledge about a restricted domain of application. For example, as a natural language component of an information system, it needs to be able to identify the relevant linguistic patterns. In case of an information system for flight scheduling words such as &amp;quot;plane&amp;quot;, &amp;quot;departure&amp;quot;, &amp;quot;late&amp;quot;, ...will typically be more relevant than for example: &amp;quot;pahn&amp;quot;, &amp;quot;thistle&amp;quot;, &amp;quot;pine&amp;quot; which might be appropriate for a different domain. In any event, there will be a whole range of words that are commonly used in conversation and, thus, are independent from the choice of a specific domain. It is therefore desirable to have a multi-level architecture which can be adapted to different domains without being forced to redesign the whole system. A text understanding system based on this kind of architecture would provide the kernel functionality that allows it to couple principles and mechanisms not immediately dependent on the domain a specific implementation of the system will be used for. The main problem of such a modular architecture is how and where to draw the boundary between domain-independent and domain-specific knowledge. There are at least two more reasons which motivate a domain-oriented design strategy: With regard to knowledge representation, the history in artificial intelligence research has lead from early enthusiastic plans of 'general problem solving capabilities' to more realistic applications of expert systems. One reason was the huge amount of data that would have to be represented together with a large set of regularities introducing a level of complexity which could not, be handeled in a realistic manner by the systems currently  available. Another problem is the inconsistency of data that would necessarily arise once a lot of different and sometimes conflicting information had to be integrated into a single knowledge base. Under this perspective task- and domain-orientation is a matter of rendering the knowledge base manageable and to allow reasoning processes to draw meaningful inferences on the basis of consistent data.</Paragraph>
    <Paragraph position="1"> The second argument in favour of a domain-oriented design strategy comes from the area of lexical semantics. It is a well known fact that the meaning of a word depends on a multitude of contextual influences. In a very broad notion of context the task and the domain of a text understanding system may be considered a part of the context that licences an effective restriction of the 'semantic scope' of a single word. This again is mainly an argument of tractability which in this case helps to minimize the amount of lexical information needed. In our example it is a natural design decision to assume that the lexicon of a language understanding system as part of an information system about flight schedules does not have to account for the 'plant'-reading of &amp;quot;plane&amp;quot;.</Paragraph>
  </Section>
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