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<Paper uid="P87-1034">
  <Title>REVISED GENERALIZED PHRASE STRUCTURE GRAMMAR</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction and Motivation
</SectionTitle>
    <Paragraph position="0"> A linguistic theory specifies a computational process that assigns structural descriptions to utterances. This process requires certain computational resources, such as time or space. In a descriptively adequate linguistic theory, the computational resources available to the theory match those used by the ideal speakerhearer. The goal of this paper is to revise generalized phrase structure grammar (GPSG) so that its computational power corresponds to the ability of the speaker-hearer.</Paragraph>
    <Paragraph position="1"> The bulk of this paper is devoted to identifying what computational resources are used by GPSG theory, and deciding whether they are linguistically necessary. GPSG contains five formal devices, each of which provides the theory with the resources to model some linguistic phenomenon or ability. I identify those aspects of each device that cause intractability and then restrict the computational power of each device to more closely match the (inherent) complexity of the phenomenon or ability it models. The remainder of the paper presents the new formal system and exercises it in the domain of topicalization, explicative pronouns, and parasitic gaps. I conclude with suggestions for efficient parser design and future research.</Paragraph>
    <Paragraph position="2"> In my opinion, the primary value of this work lies in the result (revised GPSG, or RGPSG) as well as in the methodology of using complexity analysis to improve linguistic theories. The methodology explicates how a tool of modern computer science can help us understand and improve theories of linguistic competence. More than that, complexity analysis forms the foundation of informed parser design. I feel RGPSG is of value both to linguists and computational linguists because it is more tractable and easier to understand, use, and implement. It can be efficiently implemented and appears to have better empirical coverage than its GPSG ancestor.</Paragraph>
    <Paragraph position="3"> tThe author is eupported by a graduate fellowship from the IBM Corporation. This research was supported in part by Thinking Machines Corporation and by NSF Grant DCR-85552543, under a Presidential Young Investigator Award to Profeuor Robert C. Berwick. I wish to thank Ed Barton for stylistic improvements and helpful discussion; Robert Berwick for support, critickm, and suggesting I pursue thk research; and Geoff Pullum for his patient help with GPSG theory.</Paragraph>
  </Section>
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