Spelling Error Detection and Correction by Computer: Some Notes and a Bibliography

Abstract

INTRODUCTIONNOT ONLY DOES the problem of correcting spelling errors by computer have a long history, it is evidently of considerable current interest as papers 17,95 and letters 18,30,57,66,69 on the topic continue to appear rapidly. This is not surprising, since techniques useful in detecting and correcting mis-spellings normally have other important applications. Moreover, both the power of small computers and the routine production of machine-readable text have increased enormously over the last decade to the point where automatic spelling error detection/correction has become not only feasible but highly desirable.Potential applications for spelling error detection/correction techniques arise in numerous applications. Early papers focused on the correction of output from optical character recognition (OCR), voice recognition, or Morse code, or on spelling errors in program code, but the domain of most interest today is probably the correction of machine-readable text made available by word processing. However, methods for assessing the similarity of two strings of symbols, which are widely used to compare mis-spellings with dictionary words, are of very general interest; e.g., for determining the evolutionary distance of proteins. 56,70,72 Similarly, one can imagine spelling correction techniques being extended to almost any kind of error-prone transmission, even to partially decrypted code. Also, spelling error detection involves searching large dictionaries; and this capability is obviously of widespread utility.This note attempts to provide a comprehensive bibliography of papers in English on the major aspects of spelling error detection and correction of English text. The author is solely reponsible for the content of the annotations. SPELLING ERROR DETECTIONThe goal of spelling error detection is basically to decide if a text string is a valid word; this is normally done by determining whether or not the string is in a system dictionary. As both the dictionary and the number of words to be processed are usually large in real-world systems, it is important to make the dictionary search highly efficient. Note that words need not be literally present in the dictionary; they may be stored much more economically as, for example, hash codes, patterns of bits distributed over a long string, or n-grams. However, in compressed representations, one usually has to be content with a very high probability that a given word is present or not rather than with the certainty given by a literal dictionary. Similarly, the dictionary may be searched via tries, trees, hash coding (scatter storage) or a variety of other techniques.

Automatic spelling correction in scientific and scholarly text

“…Also, the techniques involved in spelling error correction have other important applications, for example, measuring the similarity of two strings of symbols to determine the evolutionary distance of proteins. The specialized subtopics are covered extensively in recent bibliographies by Peterson [7] and Pollock [8] and are not discussed further here.…”

Approximate personal name‐matching through finite‐state graphs

“…Noteworthy among the error-correction systems based on nonphonetic coding is SPEEDCOP (Spelling Error Detection-Correction Project) (Pollock, 1982;Pollock & Zamora, 1984), currently used in the databases of Chemical Abstracts Services. The nucleus of this system is an algorithm designed to correct misspellings that consist of a single error, and a dictionary where the correct forms are stored.…”

Computerized correction of phonographic errors

“…Specifically, however, several types of errors, which are due to completely different causes, must receive appropriate modeling and treatment ( Figure 1). Since at least 1957, the very beginning of computer science, a considerable literature has been devoted to error recovery from garbled strings (for a review, see Peterson, 1980 andPollock, 1982). The early works were oriented towards two particular types of errors.…”