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<Paper uid="C00-2101">
  <Title>Learning Semantic-Level Information Extraction Rules by Type-Oriented ILP</Title>
  <Section position="8" start_page="702" end_page="703" type="relat">
    <SectionTitle>
7 Related Work
</SectionTitle>
    <Paragraph position="0"> l)revious researches on generating lli; rules from texts with templates include AutoSlog-TS (Riloff,1996), (',I{YS'FAL (Soderland et al., 1995), I'AIAKA (l(im et al., 1995), MlgP (Iluffman, 11.996) and RAPII~;I~ (Califl' and Mooney, 1997). In our approach, we use the type-oriented H,P system RItlJ +, which ix independent of natural language analysis. This point differentiates our ~pproach from the others.</Paragraph>
    <Paragraph position="1"> Learning semantic-level IE rules using an II,P system from semantic representations is also a new challenge in II'; studies.</Paragraph>
    <Paragraph position="2"> Sasald (Sasaki and Itaruno, 11997) applied RI{B + to the extraction of the number of deaths and injuries fi'om twenty five articles. That experiment was sufficient to assess the performance of the learner, but not to evaJuate its feasibility in IE tasks.</Paragraph>
  </Section>
class="xml-element"></Paper>