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<Paper uid="C90-3007">
  <Title>Partial Descriptions and Systemic Grammar</Title>
  <Section position="2" start_page="0" end_page="36" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> tIalliday's system networks \[3\] lay out in diagrammatic form the interlinked sets of linguistic choices which a speaker nmst make in order to generate an utterance. As such they have formed the basis of several computer models of natural language generation \[4,11\].ttowever, as Mellish \[12\] has pointed out, a network can also be read as encoding a set of background constraints which restrict the co- occurrence of descriptive features, and hence as a specification of the way in which partial descriptions of linguistic objects can be combined. Although it is easy to combine feature sets, it is not a.lways clear whether the resulting combined description can actually describe any well-formed linguistic object. Thus the main task we face is that of checking feature sets for consistency.</Paragraph>
    <Paragraph position="1"> Consider for example the framework given by Winograd for the description of English pronouns, which is reproduced in figure 1. Suppose thai; a natural language system has somehow recovered the information that a pronoun in a particular position can be treated as both third (person) and subjective. At this stage we could be dealing with either &amp;quot;they&amp;quot;, &amp;quot;he&amp;quot;, &amp;quot;she&amp;quot; or &amp;quot;it&amp;quot;. Were we to combine this under-specified pronoun with the further description feminine we should know for sure that the pronoun described is &amp;quot;she&amp;quot; and the number has to be singular, since the network dictates, in a way which will be explained in detail below, that the choice between feminine, masculine, and neuter is only applicable to third person singular pronouns. The network thus provides the raw material for particular sorts of limited inference about the behaviour of pronouns. We wanted to investigate the mathematical properties of systemic networks in order to better understand the nature of the constraints on feature structure which they are capaMe of expressing.</Paragraph>
    <Paragraph position="2"> Both Mellish and Kasper \[7\] provide translations of systemic networks into non-graphical formMisms: Mellish expresses constraints as axioms within a simple subset of predicate logic, while Kasper uses an extended version'of Functional Unification Grammar \[10\]. Unfortunately the methods which they then use to check for consistency are powerful general methods which may incur considerable computational cost.</Paragraph>
    <Paragraph position="3"> We initially hoped to show that systemic networks constitute a low-power constraint language which combines the twin goals of linguistic credibility and computationM tractability. While the main result we present in this paper is a negative one indicating the unexpected power of systemic networks, we do go on to present suggestions about how networks can be exploited in natural language applications.</Paragraph>
  </Section>
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