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<Paper uid="W03-0105">
  <Title>Grounding spatial named entities for information extraction and question answering</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The task of named entity annotation of unseen text has recently been successfully automated, achieving near-human performance using machine learning (Zheng and Su, 2002). But many applications also require grounding i.e., associating each classi ed text span with a referent in the world or some model thereof.</Paragraph>
    <Paragraph position="1"> The current paper discusses spatial grounding of named entities that may be referentially ambiguous, using a minimality heuristic that is informed by external geographic knowledge sources. We then apply these ideas to the creation of visual surrogates for news articles.</Paragraph>
    <Paragraph position="2"> This paper is structured as follows: Section 2 discusses how spatial named entities can be grounded and how this interacts with their extraction and applications. Section 3 describes a geo-spatial resolution algorithm. Section 4 shows how maps can be automatically constructed from named-entity tagged newswire text using resolved place names, hence introducing a new, graphical document surrogate. Section 5 deals with the usefulness of grounded named entities for question answering. Section 6 presents some related work, and Section 7 concludes this paper.</Paragraph>
  </Section>
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