<?xml version="1.0" standalone="yes"?>
<Paper uid="P98-1008">
  <Title>Time Mapping with Hypergraphs</Title>
  <Section position="3" start_page="59" end_page="60" type="metho">
    <SectionTitle>
3 Experiments with Hypergraphs
</SectionTitle>
    <Paragraph position="0"> The method of converting word graphs to hypergraphs has been used in two experiments so far. One of them is devoted to the study of connectionist unification in speech applications (Weber, forthcoming). The other one, from which the performance figures in this section are drawn, is an experimental speech translation system focusing on incremental operation and uniform representation (Amtrup, 1997).</Paragraph>
    <Paragraph position="1">  graphs produced by the Hamburg speech recognition system (Huebener et al., 1996). The test data consisted of one dialogue within the Verbmobil domain. There were 41 turns with an average length of 4.65s speaking time per turn. The word graphs contained 1828 edges on the average. Figure 6 shows the amount of reduction in the number of edges by converting the graphs into hypergraphs. On the average, 1671 edges were removed (mapped), leaving 157 edges in hypergraphs, approximately 91% less than the original word graphs.</Paragraph>
    <Paragraph position="2"> Next, we used both sets of graphs (the original word graphs and hypergraphs) as input to the speech parser used in (Amtrup, 1997).</Paragraph>
    <Paragraph position="3"> This parser is an incremental active chart parser which uses a typed feature formalism to describe linguistic entities. The grammar is focussed on partial parsing and contains rules mainly for noun phrases, prepositional phrases and such.</Paragraph>
    <Paragraph position="4"> The integration of complete utterances is neglected. Figure 7 shows the reduction in terms of chart edges at completion time.</Paragraph>
    <Paragraph position="5">  We want to show the effect of hypergraphs regarding edge reduction and parsing effort. In order to provide real-world figures, we used word  The amount of reduction concerning parsing effort is much less impressive than pure edge reduction. On the average, parsing of complete graphs resulted in 15547 chart edges, while parsing of hypergraphs produced 3316 chart edges, a reduction of about 79%. Due to edge combinations, one could have expected a much higher value. The reason for this fact lies mainly with the redundancy test used in the parser. There  are many instances of edges which are not inserted into the chart at all, because identical hypotheses are already present.</Paragraph>
    <Paragraph position="6"> Consequently, the amount of reduction in parse time is within the same bounds. Parsing ordinary graphs took 87.7s, parsing of hypergraphs 6.4s, a reduction of 93%. There are some extreme cases of word graphs, where hypergraph parsing was 94 times faster than word graph parsing. One of the turns had to be excluded from the test set, because it could not be fully parsed as word graph.</Paragraph>
  </Section>
class="xml-element"></Paper>