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<Paper uid="N01-1016">
  <Title>Edit Detection and Parsing for Transcribed Speech</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a simple architecture for parsing transcribed speech in which an edited-word detector rst removes such words from the sentence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. The edit detector achieves a misclassi cation rate on edited words of 2.2%.</Paragraph>
    <Paragraph position="1"> (The NULL-model, which marks everything as not edited, has an error rate of 5.9%.) To evaluate our parsing results we introduce a new evaluation metric, the purpose of which is to make evaluation of a parse tree relatively indi erent to the exact tree position of EDITED nodes. By this metric the parser achieves 85.3% precision and 86.5% recall.</Paragraph>
  </Section>
class="xml-element"></Paper>