Successfully detecting and correcting false friends using channel profiles

  • Ulrich Reffle
  • Annette Gotscharek
  • Christoph RinglstetterEmail author
  • Klaus U. Schulz
Original Paper


The detection and correction of false friends—also called real-word errors—is a notoriously difficult problem. On realistic data, the break-even point for automatic correction so far could not be reached: the number of additional infelicitous corrections outnumbered the useful corrections. We present a new approach where we first compute a profile of the error channel for the given text. During the correction process, the profile (1) helps to restrict attention to a small set of “suspicious” lexical tokens of the input text where it is “plausible” to assume that the token represents a false friend. In this way, recognition of false friends is improved. Furthermore, the profile (2) helps to isolate the “most promising” correction suggestion for “suspicious” tokens. Using a conventional word trigram statistics for disambiguation we obtain a correction method that can be successfully applied to unrestricted text. In experiments for OCR documents, we show significant accuracy gains by fully automatic correction of false friends.


False friends Error correction Error dictionaries 


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Ulrich Reffle
    • 1
  • Annette Gotscharek
    • 1
  • Christoph Ringlstetter
    • 1
    Email author
  • Klaus U. Schulz
    • 1
  1. 1.CIS, University of MunichMunichGermany

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