Techniques for automatically correcting words in text (abstract)

ABSTRACT

Research aimed at correcting words in text has focused on three progressively more difficult problems:

• 1) non-word error detection;
• 2) isolated-word error correction; and
• 3) context dependent word correction.

In response to the first problem, efficient pattern matching and n-gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural language processing tools or statistical language models have been carried out. This article surveys documented findings on spelling error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.

(The Article on which the Talk was based appeared in the December 1992 Issue of Computing Surveys)

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Experimentation, Languages