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<Paper uid="P98-2177">
  <Title>Statistical Models for Unsupervised Prepositional Phrase Attachment</Title>
  <Section position="8" start_page="1083" end_page="1084" type="evalu">
    <SectionTitle>
6 Evaluation in Spanish
</SectionTitle>
    <Paragraph position="0"> We claim that our approach is portable to languages with similar word order, and we support this claim by demonstrating our approach on the Spanish language. We used the Spanish tagger and morphological analyzer developed at the Xerox Research Centre Europe 4 and we modified the extraction heuristic to account for the new tagset, and to account for the Spanish equivalents of the words of (i.e., de or del) and to be (i.e., set). Chunking was not performed on the Spanish data. We used 450k sentences of raw text from the Linguistic Data Consortium's Spanish News Text Collection to extract a training set, and we used a non-overlapping set of 50k sentences from the collection to create test sets. Three native Spanish speakers were asked to extract and annotate ambiguous instances of Spanish prepositional phrase attachments. They annotated two sets (using the full sentence context); one set consisted of all ambiguous prepositional phrase attachments of the form (v,n,p, n2), and the other set consisted of cases where p = con. For testing our classifier, we used only those judgments on which all three annotators agreed.</Paragraph>
    <Section position="1" start_page="1083" end_page="1084" type="sub_section">
      <SectionTitle>
6.1 Performance
</SectionTitle>
      <Paragraph position="0"> The performance of the classifiers Clbigram, Clinterp, and Clbase , when trained and tested on Spanish language data, are shown in Table 6. The Spanish test set has fewer ambiguous prepositions than the English test set, as shown by the accuracy of Clbase. However, the accuracy improvements of Clbigra m over Clbase are statistically significant for both test sets. 5</Paragraph>
    </Section>
  </Section>
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