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<Paper uid="W97-0313">
  <Title>A Corpus-Based Approach for Building Semantic Lexicons</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon.</Paragraph>
    <Paragraph position="1"> In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.</Paragraph>
  </Section>
class="xml-element"></Paper>