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<Paper uid="J96-3007">
  <Title>Technology A Probabilistic Recursive Transition Network is an elevated version of a Recursive Transition Network used to model and process context-free languages in stochastic parameters. We present</Title>
  <Section position="6" start_page="426" end_page="426" type="evalu">
    <SectionTitle>
5. Experiments
</SectionTitle>
    <Paragraph position="0"> The goal of our experiments is to see how much saving the new estimation algorithm achieves in computational cost. Out of 14,132 Wall Street Journal trees of the Penn tree corpus, 1,543 trees corresponding to sentences with 10 words or less were chosen, and the programs written in C language were run at a Sparcl0 workstation.</Paragraph>
    <Paragraph position="1"> The basic implementation of an Inside-Outside algorithm assumes tables for Insides and Outsides so that identical Insides and Outsides need not be recomputed.</Paragraph>
    <Paragraph position="2"> A chart Outside or re-estimation algorithm assumes a refined table of Insides that contains only valid Insides used in generating the input sentence as discussed earlier, and Outside computation is done based on the refined table.</Paragraph>
    <Paragraph position="3"> The improvement from the chart re-estimation algorithm is measured in the number of actual Inside and Outside computations done to estimate the parameters. Figure 5 shows the average counts of Insides used in estimating 50 trees randomly selected from 1,543 samples. Before the re-estimation algorithm is applied, an RTN that faithfully encodes the input trees without any overgeneration is constructed from the 50 trees. The gain in Insides from the chart re-estimation algorithm is very clear, and in the case of Outsides the gain is even more conspicuous (see Figure 6). The number of Insides counted in chart version also includes the Insides computed in preparing the chart.</Paragraph>
  </Section>
class="xml-element"></Paper>