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<Paper uid="P03-1020">
  <Title>tRuEcasIng</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Truecasing is the process of restoring case information to badly-cased or non-cased text. This paper explores truecasing issues and proposes a statistical, language modeling based truecaser which achieves an accuracy of 98% on news articles. Task based evaluation shows a 26% F-measure improvement in named entity recognition when using truecasing.</Paragraph>
    <Paragraph position="1"> In the context of automatic content extraction, mention detection on automatic speech recognition text is also improved by a factor of 8. Truecasing also enhances machine translation output legibility and yields a BLEU score improvement of 80:2%. This paper argues for the use of truecasing as a valuable component in text processing applications.</Paragraph>
  </Section>
class="xml-element"></Paper>