<?xml version="1.0" standalone="yes"?>
<Paper uid="H01-1034">
  <Title>Improving Information Extraction by Modeling Errors in Speech Recognizer Output</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
ABSTRACT
</SectionTitle>
    <Paragraph position="0"> In this paper we describe a technique for improving the performance of an information extraction system for speech data by explicitly modeling the errors in the recognizer output. The approach combines a statistical model of named entity states with a lattice representation of hypothesized words and errors annotated with recognition confidence scores. Additional refinements include the use of multiple error types, improved confidence estimation, and multi-pass processing. In combination, these techniques improve named entity recognition performance over a text-based baseline by 28%.</Paragraph>
  </Section>
class="xml-element"></Paper>