<?xml version="1.0" standalone="yes"?>
<Paper uid="P00-1037">
  <Title>An Improved Error Model for Noisy Channel Spelling Correction</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The noisy channel model has been applied to a wide range of problems, including spelling correction. These models consist of two components: a source model and a channel model. Very little research has gone into improving the channel model for spelling correction. This paper describes a new channel model for spelling correction, based on generic string to string edits. Using this model gives significant performance improvements compared to previously proposed models.</Paragraph>
    <Paragraph position="1"> Introduction The noisy channel model (Shannon 1948) has been successfully applied to a wide range of problems, including spelling correction. These models consist of two components: a source model and a channel model. For many applications, people have devoted considerable energy to improving both components, with resulting improvements in overall system accuracy. However, relatively little research has gone into improving the channel model for spelling correction. This paper describes an improvement to noisy channel spelling correction via a more powerful model of spelling errors, be they typing mistakes or cognitive errors, than has previously been employed. Our model works by learning generic string to string edits, along with the probabilities of each of these edits. This more powerful model gives significant improvements in accuracy over previous approaches to noisy channel spelling correction.</Paragraph>
  </Section>
class="xml-element"></Paper>