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<Paper uid="P99-1075">
  <Title>Packing of Feature Structures for Efficient Unification of Disjunctive Feature Structures</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Efficient treatment of syntactic/semantic ambiguity is a key to making efficient parsers for wide-coverage grammars. In feature-structure-based grammars 1, such as HPSG (Pollard and Sag, 1994), ambiguity is expressed not only by manually-tailored disjunctive feature structures, but also by enumerating non-disjunctive feature structures. In addition, there is ambiguity caused by non-determinism when applying lexical/grammar rules. As a result, a large number of lexical/phrasal feature structures are required to express ambiguous syntactic/semantic structures. Without efficient processing of these feature structures, a sufficient parsing speed is unattainable.</Paragraph>
    <Paragraph position="1"> This paper proposes a method for packing feature structures, which is an automatic optimization method for parsers based on feature structure unification. This method automatically extracts equivalent parts of feature structures and collapses them into a single packed feature structure. A packed feature structure can be processed more efficiently because we can avoid redundant repetition of unification of the equivalent parts of original feature structures.</Paragraph>
    <Paragraph position="2"> There have been many studies on efficient  described in (Carpenter, 1992).</Paragraph>
    <Paragraph position="3"> unification of disjunctive feature structures (Kasper and Rounds, 1986; Hasida, 1986; DSrre and Eisele, 1990; Nakano, 1991; Blache, 1997; Blache, 1998). All of them suppose that disjunctive feature structures should be given by grammar writers or lexicographers. However, it is not practical to specify all ambiguity using only manually-tailored disjunctive feature structures in grammar development. Where disjunctive feature structures cannot be given explicitly those algorithms lose their advantages. Hence, an automatic conversion method, such as the packing method described hereafter, is required for further optimization of those systems. In addition, this packing method converts general feature structures to a suitable form for a simple and efficient unification algorithm which is also described in this paper.</Paragraph>
    <Paragraph position="4"> Griffith (Griffith, 1995; Griffith, 1996) points out the same problem and proposes a compilation method for feature structures called modularization. However, modularization is very time-consuming, and is not suitable for optimizing feature structures produced during parsing. An earlier paper of myself (Miyao et al., 1998) also discusses the same problem and proposes another packing method. However, that method can pack only pre-specified parts of input feature structures, and this characteristic limits the overall efficient gain. The new method in this paper can pack any kind of feature structures as far as possible, and is more general than the previous method.</Paragraph>
  </Section>
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