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<Paper uid="P97-1029">
  <Title>Morphological Disambiguation by Voting Constraints</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Automatic morphological disambiguation is an important component in higher level analysis of natural language text corpora. There has been a large number of studies in tagging and morphological disambiguation using various techniques such as statistical techniques, e.g., (Church, 1988; Cutting et al., 1992; DeRose, 1988), constraint-based techniques (Karlsson et al., 1995; Voutilainen, 1995b; Voutilainen, Heikkil/i, and Anttila, 1992; Voutilainen and Tapanainen, 1993; Oflazer and KuruSz, 1994; Oflazer and Till 1996) and transformation-based techniques (Brilt, 1992; Brill, 1994; Brill, 1995).</Paragraph>
    <Paragraph position="1"> This paper presents a novel approach to constraint based morphological disambiguation which relieves the rule developer from worrying about conflicting rule ordering requirements. The approach depends on assigning votes to constraints according to their complexity and specificity, and then letting constraints cast votes on matching parses of a given lexical item. This approach does not reflect the outcome of matching constraints to the set of morphological parses immediately. Only after all applicable rules are applied to a sentence, all tokens are disambiguated in parallel. Thus, the outcome of the rule applications is independent of the order of rule applications. Rule ordering issue has been discussed by Voutilainen(1994), but he has recently indicated 1 that insensitivity to rule ordering is not a property of their system (although Voutilainen(1995a) states that it is a very desirable property) but rather is achieved by extensively testing and tuning the rules.</Paragraph>
    <Paragraph position="2"> In the following sections, we present an overview of the morphological disambiguation problem, highlighted with examples from Turkish. We then present our approach and results. We finally conclude with a very brief outline of our investigation into efficient implementations of our approach.</Paragraph>
  </Section>
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