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<Paper uid="P97-1029">
  <Title>Morphological Disambiguation by Voting Constraints</Title>
  <Section position="7" start_page="226" end_page="226" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented an approach to constraint-based morphological disambiguation which uses constraint voting as its primary mechanism for parse selection and alleviates the rule developer from worrying about rule ordering issues. Our approach is quite general and is applicable to any language. Rules describing language specific linguistic constraints vote on matching parses of tokens, and at the end, parses TSuggested by Lauri Karttunen (private communication). null for every token receiving the highest tokens are selected. We have applied this approach to Turkish, a language with complex agglutinative word forms exhibiting morphological ambiguity phenomena not usually found in languages like English and have obtained quite promising results. The convenience of adding new rules in without worrying about where exactly it goes in terms of rule ordering (something that hampered our progress in our earlier work on disambiguating Turkish morphology (Oflazer and KuruSz, 1994; Oflazer and Tiir, 1996)), has also been a key positive point. Furthermore, it is also possible to use rules with negative votes to disallow impossible cases. This has been quite useful for our work on tagging English (Tfir, Oflazer, and 0z-kan, 1997) where such rules with negative weights were used to fine tune the behavior of the tagger in various problematic cases.</Paragraph>
    <Paragraph position="1"> The proposed approach is also amenable to an efficient implementation by finite state transducers (Kaplan and Kay, 1994). By using finitestate transducers, it is furthermore possible to use a bit more expressive rule formalism including for instance the Kleene * operator so that one can use a much smaller set of rules to cover the same set of local linguistic phenomena.</Paragraph>
    <Paragraph position="2"> Our current and future work in this framework involves the learning of constraints and their votes from corpora, and combining learned and hand-crafted rules.</Paragraph>
  </Section>
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