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<Paper uid="A94-1011">
  <Title>ing, Word Associations and Typical Predicate-Argument</Title>
  <Section position="8" start_page="68" end_page="69" type="evalu">
    <SectionTitle>
6 Results
</SectionTitle>
    <Paragraph position="0"> The results of the experiments on the RAPRA corpus are presented below. 3 Despite the peculiarities of the corpus, the message is clear. The result that the standard model fares no better on word sequence sets than on word sets is repeated, and it is clear that the Single Term model fares much better than the Logit model on this data set. However, what is most interesting is that the Single Term models fares significantly better on the more sophisticated sequence based representations of the document than on the simpler word based representation. There is, however, no significant advantage identified by parsing the corpus into noun-groups over simply considering all word sequences. The recall scores for the rule-based tagging strategy show that the improved performance of the sequence based representations can be explained by 3All recall and precision scores are microaveraged (Lewis 1992c); they are the expected probability of assigning or recalling correctly per tagging decision. The training set was a set of 200,000 abstracts, and the separate test set had 10,000 abstracts. The experiments looked at only the 520 most common descriptors. In the table, TW means that a term-weighting model was used, while ST means that the single term model was used. 'Word' means the representation was a wordset, 'Seq', the set of all sequences, and 'NG' the set of groups derived from the grammar. For the sequence representations, either all the possible sequences or groups were used (denoted by 'all'), or just the most specific ones were used (denoted by 'spec').</Paragraph>
  </Section>
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