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<Paper uid="W04-0408">
  <Title>Multiword expressions as dependency subgraphs</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We extended the XDG grammar formalism with a means to break out of the straightjacket of the 1:1-correspondence between words and nodes. To this end, we proposed the new notion of groups, allowing to enrich the XDG lexicon with tuples of dependency subgraphs. We illustrated how to tackle complicated MWEs such as support verb constructions with this new idea, and how to compile groups into simple lexical entries.</Paragraph>
    <Paragraph position="1"> We see two main benefits of our approach. The first is that we can retain the XDG formalization, and also its axiomatization as a constraint satisfaction problem, in its entirety. Thus, we can simply continue to use the existing XDG solver for parsing and generation. The second benefit is that we can use the same group lexicon for both parsing and generation, the only difference being that for generation, we have to slightly adapt the compilation into lexical entries.</Paragraph>
    <Paragraph position="2"> There are many issues which have remained untouched in this paper. For one, we did not talk about our treatment of word order in this paper for lack of space. Word order is among the best researched issues in the context of XDG. For a thorough discussion about word order in XDG, we refer to (Duchier and Debusmann, 2001) and (Debusmann, 2001).</Paragraph>
    <Paragraph position="3"> Another issue is that of the relation between groups and the meta-grammatical functionality of the XDG lexicon, offering lexical inheritance, templates and also disjunction in the sense of crossings (Candito, 1996) to lexically state linguistic generalizations. How well groups can be integrated with this meta-grammatical functionality is an open question.</Paragraph>
    <Paragraph position="4"> XDG research has so far mainly been focused on parsing, only to a very small extent on generation, and to no extent at all on Machine Translation (MT). There is still a lot to do on both of the latter topics, and even for parsing, we are only at the beginning. Although with our smaller-scale example grammars, parsing and generation takes polynomial time, we have yet to find out how we can scale this up to large-scale grammars. We have started importing and inducing large-scale grammars from existing resources, but can so far only speculate about if and how we can parse them efficiently. In a related line of research, we are also working on the incorporation of statistical information (Dienes et al., 2003) to help us to guide the search for solutions. This could improve performance because we would only have to search for a few good analyses instead of enumerating hundreds of them.</Paragraph>
  </Section>
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