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<Paper uid="P97-1014">
  <Title>Centering in-the-Large: Computing Referential Discourse Segments</Title>
  <Section position="8" start_page="109" end_page="110" type="relat">
    <SectionTitle>
6 Related Work
</SectionTitle>
    <Paragraph position="0"> There has always been an implicit relationship between the local perspective of centering and the global view of focusing on discourse structure (cf. the discussion in Grosz et al. (1995)). However, work establishing an explicit account of how both can be joined in a computational model has not been done so far. The efforts of Sidner (1983), e.g., have provided a variety of different focus data structures to be used for reference resolution.</Paragraph>
    <Paragraph position="1"> This multiplicity and the on-going growth of the number of different entities (cf. Suri &amp; McCoy (1994)) mirrors an increase in explanatory constructs that we consider a methodological drawback to this approach because they can hardly be kept control of. Our model, due to its hierarchical nature implements a stack behavior that is also inherent to the above mentioned proposals. We refrain, however, from establishing a new data type (even worse, different types of stacks) that has to be managed on its own. There is no need for extra computations to determine the &amp;quot;segment focus&amp;quot;, since that is implicitly given in the local centering data already available in our model.</Paragraph>
    <Paragraph position="2"> A recent attempt at introducing global discourse notions into the centering framework considers the use of a cache model (Walker, 1996b). This introduces an additional data type with its own management principles for data storage, retrieval and update. While our proposal for centered discourse segmentation also requires a data structure of its own, it is better integrated into centering than the caching model, since the cells of segment structures simply contain &amp;quot;pointers&amp;quot; that implement a direct link to the original centering data. Hence, we avoid extra operations related to feeding and updating the cache.</Paragraph>
    <Paragraph position="3"> The relation between our centered segmentation algorithm and Walker's (1996a) integration of centering into the cache model can be viewed from two different angles.</Paragraph>
    <Paragraph position="4"> On the one hand, centered segmentation may be a part of the cache model, since it provides an elaborate, non-linear ordering of the elements within the cache. Note, however, that our model does not require any prefixed size corresponding to the limited attention constraint. On the other hand, centered segmentation may replace the  cache model entirely, since both are competing models of the attentional state. Centered segmentation has also the additional advantage of restricting the search space of anaphoric antecedents to those discourse entities actually referred to in the discourse, while the cache model allows unrestricted retrieval in the main or long-term memory.</Paragraph>
    <Paragraph position="5"> Text segmentation procedures (more with an information retrieval motivation, rather than being related to reference resolution tasks) have also been proposed for a coarse-grained partitioning of texts into contiguous, non-overlapping blocks and assigning content labels to these blocks (Hearst, 1994). The methodological basis of these studies are lexical cohesion indicators (Morris &amp; Hirst, 1991) combined with word-level co-occurrence statistics. Since the labelling is one-dimensional, this approximates our use of preferred centers of discourse segments. These studies, however, lack the fine-grained information of the contents of Cf lists also needed for proper reference resolution.</Paragraph>
    <Paragraph position="6"> Finally, many studies on discourse segmentation highlight the role of cue words for signaling segment boundaries (cf., e.g., the discussion in Passonneau &amp; Litman (1993)). However useful this strategy might be, we see the danger that such a surface-level description may actually hide structural regularities at deeper levels of investigation illustrated by access mechanisms for centering data at different levels of discourse segmentation.</Paragraph>
  </Section>
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