Computer programs for spelling correction: An experiment in program design

  • James L. Peterson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 96)


Hash Table Program Design Optical Character Recognition Spelling Error Spell Correction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    C. N. Alberga, “String Similarity and Misspellings”, Communications of the ACM, Volume 10, Number 5, (May 1967), pages 302–313. Master's Thesis. Reviews previous work. Mentions two researchers at IBM Watson who suggest finding the longest common substrings and assigning probabilities based on the portion of the correct string matched. Does rather extensive but unreadable analysis of different algorithms, but with no real results. Reviewed in Computing Reviews, Volume 8, Number 5, Review 12,712.Google Scholar
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    C. R. Blair, “A Program for Correcting Spelling Errors”, Information and Control, Volume 3, Number 1, (March 1960), pages 60–67. Weights the letters to create a four or five letter abbreviation for each word. If abbreviations match, the words are assumed to be the same. Mentions the possibility (impossibility) of building in rules like: i before e except after c and when like a as in neighbor and weigh, ...Google Scholar
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    H. T. Glantz, “On the Recognition of Information with a Digital Computer”, Journal of the ACM, Volume 4, Number 2, (April 1957), pages 178–188. Seems to want either exact match or greatest number of equal characters in equal positions. Good for OCR.Google Scholar
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    L. E. McMahon, L. L. Cherry, and R. Morris, “Statistical Text Processing”, The Bell System Technical Journal, Volume 57, Number 6, Part 2, (July–August 1978), pages 2137–2154. Good description of how computer systems can be used to process text, including spelling correction and an attempt at a syntax checker.Google Scholar
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    F. Muth and A. L. Tharp, “Correcting Human Error in Alphanumeric Terminal Input”, Information Processing and Management, Volume 13, Number 6, (1977), pages 329–337. Suggests a tree structure (like a trie) with special search procedures to allow corrections to be found. Damerau's review points out that their search strategies need improvement and that their tree is much too big to be practical. Each node of the tree has one character (data) and three pointers (father, brother, son). Reviewed in Computing Reviews, Volume 19, Number 6, Review 33,119.CrossRefGoogle Scholar
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    D. P. Partridge and E. B. James, “Natural Information Processing”, International Journal of Man-Machine Studies, Volume 6, Number 2, (March 1974), pages 205–235. Uses a tree structure representation of words to allow checks for incorrect input words. Done in the context of correcting keywords in a Fortran program, but more is there. Frequencies are kept with tree branches to allow the tree to modify itself to optimize search.Google Scholar
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    E. M. Riseman and R. W. Ehrich, “Contextual Word Recognition Using Binary Digrams”, IEEE Transactions on Computers, Volume C-20, Number 4, (April 1971), pages 397–403. Indicates the important property of digrams is only their zero or non-zero nature.Google Scholar
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    P. J. Tenczar and W. W. Golden, “Spelling, Word and Concept Recognition”, Report CERL-X-35, University of Illinois, Urbana, Illinois, (October 1962).Google Scholar
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    R. B. Thomas and M. Kassler, “Character Recognition in Context”, Information and Control, Volume 10, Number 1, (January 1967), pages 43–64. Considers tetragrams (sequences of 4 letters). Of 274 possible tetragrams, only 12 percent (61,273) are legal.CrossRefGoogle Scholar
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    L. Thorelli, “Automatic Correction of Errors in Text”, BIT, Volume 2, Number 1, (1962), pages 45–62. Sort of a survey/tutorial. Mentions digrams and dictionary look-up. Suggests maximizing probabilities.Google Scholar
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    C. M. Vossler and N. M. Branston, “The Use of Context for Correcting Garbled English Text”, Proceedings of the 19th ACM National Convention, (August 1964), pages D2.4-1 to D2.4-13. Uses confusion matrix and word probabilities to select the most probable input word. Also uses digrams. Trying to improve OCR input.Google Scholar
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    R. A. Wagner and M. J. Fischer, “The String-to-String Correction Problem”, Journal of the ACM, Volume 21, Number 1, (January 1974), pages 168–173. Algorithm for determining similarity of two strings as minimum number of edit operations to transform one into the other. Allowed edit operations are add, delete or substitute one character.CrossRefGoogle Scholar
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    C. K. Wong and A. K. Chandra, “Bounds for the String Editing Problem”, Journal of the ACM, Volume 23, Number 1, (January 1976), pages 13–16. Shows that the complexity bounds of [Wagner and Fischer 1974] are not only sufficient but also necessary.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1980

Authors and Affiliations

  • James L. Peterson
    • 1
  1. 1.The Department of Computer SciencesThe University of TexasAustinUSA

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