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<Paper uid="W97-0313">
  <Title>A Corpus-Based Approach for Building Semantic Lexicons</Title>
  <Section position="13" start_page="122" end_page="122" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> Building semantic lexicons will always be a subjective process, and the quality of a semantic lexicon is highly dependent on the task for which it will be used. But there is no question that semantic knowledge is essential for many problems in natural language processing. Most of the time semantic knowledge is defined manually for the target application, but several techniques have been developed for generating semantic knowledge automatically. Some systems learn the meanings of unknown words using expectations derived from other word definitions in the surrounding context (e.g., (Granger, 1977; Carbonell, 1979; Jacobs and Zernik, 1988; Hastings and Lytinen, 1994)). Other approaches use example or case-based methods to match unknown word contexts against previously seen word contexts (e.g., (Berwick, 1989; Cardie, 1993)). Our task orientation is a bit different because we are trying to construct a semantic lexicon for a target category, instead of classifying unknown or polysemous words in context.</Paragraph>
    <Paragraph position="1"> To our knowledge, our system is the first one aimed at building semantic lexicons from raw text without using any additional semantic knowledge.</Paragraph>
    <Paragraph position="2"> The only lexical knowledge used by our parser is a part-of-speech dictionary for syntactic processing.</Paragraph>
    <Paragraph position="3"> Although we used a hand-crafted part-of-speech dictionary for these experiments, statistical and corpus-based taggers are readily available (e.g., (Brill, 1994; Church, 1989; Weischedel et al., 1993)).</Paragraph>
    <Paragraph position="4"> Our corpus-based approach is designed to support fast semantic lexicon construction. A user only needs to supply a representative text corpus and a small set of seed words for each target category. Our experiments suggest that a core semantic lexicon can be built for each category with only 10-15 minutes of human interaction. While more work needs to be done to refine this procedure and characterize the types of categories it can handle, we believe that this is a promising approach for corpus-based semantic knowledge acquisition.</Paragraph>
  </Section>
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