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<Paper uid="W99-0113">
  <Title>Discourse Anaphora Resolution*</Title>
  <Section position="6" start_page="115" end_page="115" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> * Within our unified framework we are able to provide a detailed account of how anaphora resolution works across stretches of discourse, Because the LDM requires specific calculation of the information available at intermediary nodes. Computationally, during parsing, a rich data structure is created representing the meaning of the discourse. This, we would argue, is a distinct advantage of Dynamic Semantic approaches such as the LDM/DQL system over current computational alternatives such as Discourse Structures Theory (Gro.~ and Sidner 1989) and Rhetorical Structure Theory (Mann and Thompson 1987) which rely upon inferring the at- tentional and intentional states of language users, in one case, and on labe!ing the coherence relations among clauses, in the other. Looking towards formal discourse syst~m-__q, we believe that while it would be possible to integrate the insights of DQL into a DRT approach such as that t~ by Asher (1993), the appr.~ taken here is computationally more tractable than more standard implementation of DRT for discourse parsing. The increased tractability results from the separation of discourse syntax and semantics which our approach imposes, taken together with the restriction of appeals to inference and world knowledge to specific moments in interpretation. In the case of the LDM, appeals to external knowledge are made only at the moment of DCU attachment.</Paragraph>
    <Paragraph position="1"> to the parse.tree.</Paragraph>
  </Section>
class="xml-element"></Paper>