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<Paper uid="C96-2190">
  <Title>Prepositional Phrase Attachment Through A Hybrid Disambiguation Model</Title>
  <Section position="8" start_page="1071" end_page="1071" type="evalu">
    <SectionTitle>
6 Experiment and Evaluation
</SectionTitle>
    <Paragraph position="0"> We did an exl~eriment to test our lnethod. First, we prcl)are(l test data of 3043 ambiguous PPs in texts randomly taken from a (:Olnl)uter manual, a.</Paragraph>
    <Paragraph position="1"> graalllnlar book and Japan Time.s.</Paragraph>
    <Paragraph position="2">  The results are shown ill Table 3. We successfully disalnbiguated 86.9% of th(, test data. To reduce sl)ars(' data. 1)roblenl and deal wilh undefined wor(ls in the dictiolm.ry, we use a l)roc(!dure simihtr to th;tt of Collins and Brook 11995) to pro(:ess head words both in training data and in test datm Tile 1)ro(:c(lure is shown as follows:  The result is rather good, COlnt)aral)h' to the l)erformance of all &amp;quot;averag('.&amp;quot; hlIl\[la, ll looking at (v,nl,p,n2) alone (al)out 85% to 90% according to Hindle ~md R ooth 1993, Collins and Brooks 1995). We attribute this result to the hyl)rid apl)roach we used, in which preferences with higher rdiabilities are used 1)rior to other on('s in the disalnl)iguation l)rocess. We found that two thresholds are very hell)ful in iml/roving the result. If we set the first threshohl as 0 ~md throw away the second threshold, then l.he success rates ill tril)le-('onfl)ination will \])(K:(,llt(' 89.1% (-1.8%), a,nd 81.2% (-3.7%) in l)aJr-(:ombilmtion. Moreover, using h)('al rules to tackle unattached PPs by statistical model is also hellfful in improving the overall su('cess rat(, since loom rules in l)hase 3 work nmch b(,tter than default (h,(:ision in Phase 4.</Paragraph>
  </Section>
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