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<Paper uid="W03-1023">
  <Title>Using the Web in Machine Learning for Other-Anaphora Resolution</Title>
  <Section position="9" start_page="14" end_page="14" type="concl">
    <SectionTitle>
7 Conclusions
</SectionTitle>
    <Paragraph position="0"> We presented a machine learning approach to otheranaphora, which uses a NB classifier and two sets of features. The first set consists of standard morpho-syntactic, recency, and semantic features based on WordNet. The second set also incorporates semantic knowledge obtained from the Web via lexico-semantic patterns specific to other-anaphora.</Paragraph>
    <Paragraph position="1"> Adding this knowledge resulted in a dramatic improvement of 11.4% points in the classifier's BYmeasure, yielding a final BY-measure of 56.9%.</Paragraph>
    <Paragraph position="2"> To our knowledge, we are the first to integrate a Web feature into a ML framework for anaphora resolution. Adding this feature is inexpensive, solves the data sparseness problem, and allows to handle examples with non-standard relations between anaphor and antecedent. The approach is easily applicable to other anaphoric phenomena by developing appropriate lexico-syntactic patterns (Markert et al., 2003).</Paragraph>
  </Section>
class="xml-element"></Paper>