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<Paper uid="C94-2114">
  <Title>A Best-Match Algorithm for Broad-Coverage Example-Based Disambiguation</Title>
  <Section position="6" start_page="719" end_page="720" type="evalu">
    <SectionTitle>
5 Experimental Results
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="719" end_page="720" type="sub_section">
      <SectionTitle>
5.1 Example-Base and Thesaurus
</SectionTitle>
      <Paragraph position="0"> All example-base for disambigu~tion of sentences in computer manuMs is now being developed. Table 1 shows its currem; size. The sentences are extracted from examples in the L(mgman Dictionary of Contemporary English \[9\] and definitions in the IBM Dictionary of Computing \[2\]. Synonym and is-a relati(mships arc extracted from the New Collins Thesaurus \[1\] and Webster's Seventh New Collegiate Dictionary \[4\].</Paragraph>
      <Paragraph position="1"> Our exainple-base is a set of head-modifier binary dependencies with relations between word, such as (subject), (object), and (PP &amp;quot;in&amp;quot;). It was developed by a bootstrapping method with human correction.</Paragraph>
      <Paragraph position="2"> In SENA, the example-base is used to resolve three types of ambiguity: attachment, wor(l-scnse~ and coordination. The h,vel of knowledge depends on the type of ambiguity.</Paragraph>
      <Paragraph position="3">  To resolve semantic ambiguities, the examl)les should be disambiguated semantically. On the other band, structural def)endencies can be extracted from raw or tagged corpora t)y using simple rules or patterns, in our approach, multile, vel descriptions of examples are allowed: one example may provide both structural and word-sense information, while another may provide only structural dependem:ies.</Paragraph>
      <Paragraph position="4"> Word-senses are added to a half of the sentences in example-base.</Paragraph>
    </Section>
    <Section position="2" start_page="720" end_page="720" type="sub_section">
      <SectionTitle>
5.2 Experiment
</SectionTitle>
      <Paragraph position="0"> We did a small experiment on disambiguation of prepositional I)hrase attachment. First, we prepared 105 ambiguous test dater randomly from 3,000 sentences in a (:olni)ute.r manual. The format of the data was as follows: verb noun prep unknown-noun None of these data (:an be disambiguated by using the conventional best-mateldng algorithm, since noun2 does not appear in the example-base or thesaurus. Conjunctive, relationslfips, described in Chapter 3, are used with the exmnple-base and the thesaurus.</Paragraph>
      <Paragraph position="1"> The results of the disambiguation are shown in Fig. 3. We were able to disambiguate 52.4% of the, test data by using mlknown-word-matching. By using Heuristic-1 in addition, we obt~ine(l a 72.4% success rate for unknown words.</Paragraph>
      <Paragraph position="2"> ODe cause of failure is imbalai,ce among exampies. The number of exanq)les for frequent verbs is larger than the number of exanq)les tk)r frequent nouns. As a result, verb attactunent tends to be preferred. 2 Another cause of failure is the mmfl)er of context dependen(:ies. In tim experim(mt, at most the nearest eight sentences were used; the optinmm number is still an open question.</Paragraph>
      <Paragraph position="3"> 2We did not use other heuristics such as prefl?r(mce lop inner attachment.</Paragraph>
    </Section>
  </Section>
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