<?xml version="1.0" standalone="yes"?>
<Paper uid="W02-1111">
  <Title>Fine-Grained Proper Noun Ontologies for Question Answering</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> The WordNet lexical ontology (Miller, 1990) contains more than 100,000 unique noun forms. Most of these noun forms are common nouns (nouns describing non-specific members of a general class, e.g.</Paragraph>
    <Paragraph position="1"> &amp;quot;detective&amp;quot;). Only a small percentage1 of the nouns in WordNet are proper nouns (nouns describing specific instances, e.g. &amp;quot;[the detective] Columbo&amp;quot;). The WordNet ontology has been widely useful, with applications in information retrieval (Sussna, 1993), text classification (Scott and Matwin, 1998), and question answering (Pasca and Harabagiu, 2001). These successes have shown that common noun ontologies have wide applicability and utility.</Paragraph>
    <Paragraph position="2"> There exists no ontology with similar coverage and detail for proper nouns. Prior work in proper noun identification has focused on 'named entity' 1A random 100 synset sample was composed of 9% proper nouns.</Paragraph>
    <Paragraph position="3"> recognition (Chinchor et al., 1999), stemming from the MUC evaluations. In this task, each proper noun is categorized, for example, as a PERSON, a LOCA-TION, or an ORGANIZATION.</Paragraph>
    <Paragraph position="4"> These coarse categorizations are useful, but more finely grained classification might have additional advantages. While Bill Clinton is appropriately identified as a PERSON, this neglects his identity as a president, a southerner, and a saxophone player.</Paragraph>
    <Paragraph position="5"> If an information request identifies the object of the search not merely as a PERSON, but as a typed proper noun (e.g. &amp;quot;a southern president&amp;quot;), this preference should be used to improve the search.</Paragraph>
    <Paragraph position="6"> Unfortunately, building a proper noun ontology is more difficult than building a common noun ontology, since the set of proper nouns grows more rapidly. New people are born. As people change, their classification must change as well. A broad-coverage proper noun ontology must be constantly updated. Therefore, to propose a viable system, a method, however limited, must be presented to build a proper noun ontology.</Paragraph>
    <Paragraph position="7"> In this paper, we explore the idea of a fine-grained proper noun ontology and its use in question answering. We build a proper noun ontology from unrestricted text using simple textual co-occurrence patterns (Section3). This automatically constructed ontology is then used on a question answering task to give preliminary results on the utility of this information (Section 4).</Paragraph>
  </Section>
class="xml-element"></Paper>