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<Paper uid="P98-2143">
  <Title>United Kingdom</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1. Introduction
</SectionTitle>
    <Paragraph position="0"> For the most part, anaphora resolution has focused on traditional linguistic methods (Carbonell &amp; Brown 1988; Carter 1987; Hobbs 1978; Ingria &amp; Stallard 1989; Lappin &amp; McCord 1990; Lappin &amp; Leass 1994; Mitkov 1994; Rich &amp; LuperFoy 1988; Sidner 1979; Webber 1979). However, to represent and manipulate the various types of linguistic and domain knowledge involved requires considerable human input and computational expense.</Paragraph>
    <Paragraph position="1"> While various alternatives have been proposed, making use of e.g. neural networks, a situation semantics framework, or the principles of reasoning with uncertainty (e.g. Connoly et al. 1994; Mitkov 1995; Tin &amp; Akman 1995), there is still a strong need for the development of robust and effective strategies to meet the demands of practical NLP systems, and to enhance further the automatic processing of growing language resources.</Paragraph>
    <Paragraph position="2"> Several proposals have already addressed the anaphora resolution problem by deliberately limiting the extent to which they rely on domain and/or linguistic knowledge (Baldwin 1997; Dagan &amp; Itai 1990; Kennedy &amp; Boguraev 1996; Mitkov 1998; Nasukawa 1994; Williams et al. 1996). Our work is a continuation of these latest trends in the search for inexpensive, fast and reliable procedures for anaphora resolution. It is also an example of how anaphors in a specific genre can be resolved quite successfully without any sophisticated linguistic knowledge or even without parsing. Finally, our evaluation shows that the basic set of antecedent tracking indicators can work well not only for English, but also for other languages (in our case Polish and Arabic).</Paragraph>
  </Section>
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