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<Paper uid="W97-0615">
  <Title>Filtering Errors and Repairing Linguistic Anomalies for Spoken Dialogue Systems</Title>
  <Section position="6" start_page="79" end_page="80" type="evalu">
    <SectionTitle>
5 Evaluation
</SectionTitle>
    <Paragraph position="0"> The parser has been tested on a 200 words application s . The robust parsing runs in real time on an SGI Indigos2 Impact (R4400 250 MHz). Table 6 shows the processing performances for each parsing pass.</Paragraph>
    <Paragraph position="1"> Results on the repairing capacities according to the filtering behaviour are presented in table 5. &amp;quot;Weakly recovered&amp;quot; means that all the information is present in the semantic representation, but part of it may be marked as uncertain with other parasite information (see figure 5 for an example).</Paragraph>
    <Paragraph position="2"> &amp;quot;Potentially correct interpretation&amp;quot; means that a valid semantic representation has been reached with SThe application task and the recognizer systems are described section 3.3. some biased information. This bias might be ignored or detected by the higher level modules. The last two lines of the table distinguish between two kinds of wrongly filtered sentence: the first appear well-formed to the parser -there is no way to recover from those-, the second contain anomalies detected by the parser -there might be some way to repair or reject those ones. It can be observed that the approach is basically non-destructive toward well-recognized sentences. There is a theoretical case that would result in a loss of information: the false rejection of an optional word. But it didn't show up. For ill-recognized sentences, at least 27% are fully recovered, for Nuance as well as for Abbot (this concerns line 3 of table 5). In both cases too, a little less than 50% appear difficult to recover, given the current filtering (last two lines of the table).</Paragraph>
  </Section>
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