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<Paper uid="E95-1012">
  <Title>Stochastic HPSG</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The purpose of our paper is to develop a principled technique for attaching a probabilistic interpretation to feature structures. Our techniques apply to the feature structures described by Carpenter (Carpenter, 1992). Since these structures are the ones which are used in by Pollard and Sag (Pollard and Sag, 1994) their relevance to computational grammars is apparent. On the basis of the usefulness of probabilistic context-free grammars (Charniak, 1993, ch. 5), it is plausible to assume that that the extension of probabilistic techniques to such structures will allow the application of known and new techniques of parse ranking and grammar induction to more interesting grammars than has hitherto been the case.</Paragraph>
    <Paragraph position="1"> The paper is structured as follows. We start by reviewing the training and use of probabilistic context-free grammars (PCFGs). We then de: velop a technique to allow analogous probabilistic annotations on type hierarchies. This gives us a clear account of the relationship between a large class of feature structures and their probabilities, but does not treat re-entrancy. We conclude by sketching a technique which does treat such structures. While we know of previous work which associates scores with feature structures (Kim, 1994) are not aware of any previous treatment which makes explicit the link to classical probability theory. null We take a slightly unconventional perspective on feature structures, because it is easier to cast our theory within the more general framework of incremental description refinement (Mellish, 1988) than to exploit the usual metaphors of constraint-based grammar. In fact we can afford to remain entirely agnostic about the means by which the HPSG grammar associates signs with linguistic strings, because all that we need in order to train our stochastic procedures is a corpus of signs which are known to be valid descriptions of strings.</Paragraph>
  </Section>
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