<?xml version="1.0" standalone="yes"?>
<Paper uid="W97-0123">
  <Title>Maximum Entropy Model Learning of Subcategorization Preference* I t-</Title>
  <Section position="6" start_page="257" end_page="259" type="evalu">
    <SectionTitle>
5.3.2 Results
</SectionTitle>
    <Paragraph position="0"> Figure I (a)-~(c) compares the precisions re and rh among the one-frame/independent-fr~me/partialframe/independent-case models. We also compare the changes of the rate of the verb-noun collocations in the test set which satisfy the case covering relation ~_co with the set ,q of active features.</Paragraph>
    <Paragraph position="1">  For the independent-frame model, we examined two different values of the independence parameter a, i.e., c~ - 0.5 as a weak condition on independence judgment and ~ - 0.9 as a strict condition on independence judgment. Figure 1 (d) shows the changes of the precisions r~, rh, and re as well as the case-coverage of the test data during the training for the independent-frame model (the independence parameter ~ - 0.9). Both of the precisions re and rh of the independent-frame model are higher than those of any other models. On the other hand, the case-coverage of the independent-frame model (as well as the that of one-frame model) is much lower than that of the partial-frame/independent-case models. The decrease of the case-coverage in the independentframe/one-frame models is caused by the overfitting to the training data. s In the case of the independent-frame model, precisions decrease in the order of re, rh, and r~. This means that the independent-frame model performs well in the task of subcategorization preference when the verb-noun collocations satisfy the case covering relation &amp;quot;&lt;cr with the set S of active features. When the verb-noun collocations do not satisfy the case covering relation, we have to use the heuristics of case covering in section 4.4.2 and then the precision of subcategorization preference decreases. If we do not care whether the verb-noun collocations satisfy the case covering relation and do not use the heuristics of case covering, this means that we use the basic model in 6The reason why the overfitting to the training data occurs in the independent-frame/one-frame models can be explained by comparing the effects of the two values of the independence parameter ~ in the independent model. When c~ equals to 0.9, both rc and rh are slightly h/gher than when a equals to 0.5. Especially, when the number of selected features are less than 300, rc is much higher when ~ equals to 0.9 than when ~ equals to 0.5, although the case-coverage of the test data is much lower. When the condition on independence judgment becomes more strict, the cases in the trig data are judged as dependent on each other more often and then this causes the estimated model to overfit to the training data. In the case of the independent-frame model, overfit to the training data seems to result in higher performance in subcategor/zation preference task, although the ease-coverage of the test data is caused to become lower.</Paragraph>
    <Paragraph position="2">  section 4.4.1 and it perfor~ worst as indicated by the precision rb.</Paragraph>
  </Section>
class="xml-element"></Paper>