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<Paper uid="P01-1045">
  <Title>From Chunks to Function-Argument Structure: A Similarity-Based Approach</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Current research on natural language parsing tends to gravitate toward one of two extremes: robust, partial parsing with the goal of broad data coverage versus more traditional parsers that aim at complete analysis for a narrowly defined set of data. Chunk parsing (Abney, 1991; Abney, 1996) offers a particularly promising and by now widely used example of the former kind.</Paragraph>
    <Paragraph position="1"> The main insight that underlies the chunk parsing strategy is to isolate the (finite-state) analysis of non-recursive syntactic structure, i.e. chunks, from larger, recursive structures. This results in a highly-efficient parsing architecture that is realized as a cascade of finite-state transducers and that pursues a leftmost longest-match pattern-matching strategy at each level of analysis.</Paragraph>
    <Paragraph position="2"> Despite the popularity of the chunk parsing approach, there seems to be a gap in current research: null Chunk parsing research has focused on the recognition of partial constituent structures at the level of individual chunks. By comparison, little or no attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis; they also constitute a necessary prerequisite for assigning function-argument structure.</Paragraph>
    <Paragraph position="3"> Automatic assignment of function-argument structure has long been recognized as a desider- null parsers (Tapanainen and J&amp;quot;arvinen, 1997; Br&amp;quot;oker et al., 1994; Lesmo and Lombardo, 2000), where functional labels are treated as first-class citizens as relations between words, and recent work on a semi-automatic method for treebank construction (Brants et al., 1997), little has been reported on based algorithm for assigning functional labels such as subject, object, head, complement, etc.</Paragraph>
    <Paragraph position="4"> to complete syntactic structures on the basis of pre-chunked input. The evaluation of the algorithm has concentrated on measuring the quality of these functional labels.</Paragraph>
  </Section>
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