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<Paper uid="W04-0713">
  <Title>An Algorithm for Resolving Individual and Abstract Anaphora in Danish Texts and Dialogues</Title>
  <Section position="3" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> The two algorithms for resolving IPAs and APAs in English dialogues, es00 and phora, recognise IPAs and APAs on the basis of semantic constraints on the argument position occupied by the anaphors. Both algorithms account for differences in reference between personal and demonstrative pronouns. In es00 demonstrative pronouns preferentially refer to abstract entities, while personal pronouns preferentially refer to individual ones. es00 resolves IPAs applying Strube's (1998) algorithm.</Paragraph>
    <Paragraph position="1"> In phora the antecedents of personal pronouns are searched for looking at their degree of salience which is implemented by word order as in (Grosz et al., 1995). Demonstratives, instead, are searched for in the list of activated entities (Gundel et al., 1993) containing non NP antecedents, which are assumed to be less salient. In phora demonstratives can also refer to Kinds.</Paragraph>
    <Paragraph position="2"> es00 requires that the structure of dialogues has been marked. Byron's phora-algorithm does not rely on predefined dialogue structure, but only searches for abstract antecedents of APAs in the sentence preceding the anaphor.</Paragraph>
    <Paragraph position="3"> Thus it does not account for APAs referring to larger discourse segments. phora relies on both semantic knowledge and a model of speech acts and accounts for more phenomena than es00.</Paragraph>
    <Paragraph position="4"> Differing from es00, phora has been implemented. A very different strategy for resolving IPAs and APAs in spoken dialogues is proposed in (Strube and M&amp;quot;uller, 2003). We will not further discuss this proposal, but Strube and M&amp;quot;uller's machine learning approach is an interesting attempt to automatically resolve anaphors without relying on any domain specific resource or preannotated data.</Paragraph>
  </Section>
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