Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work 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 patterns, provides descriptions of various nonword detection and isolated-word 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.

Techniques for automatically correcting words in text

Approximate matching of strings is reviewed with the aim of surveying techniques suitable for finding an item in a database when there may be a spelling mistake or other error in the keyword. The methods found are classified as either equivalence or similarity problems. Equivalence problems are seen to be readily solved using canonical forms. For sinuiarity problems difference measures are surveyed, with a full description of the wellestablmhed dynamic programming method relating this to the approach using probabilities and likelihoods. Searches for approximate matches in large sets using a difference function are seen to be an open problem still, though several promising ideas have been suggested. Approximate matching (error correction) during parsing is briefly reviewed.

http://www.quretec.com/u/vilo/edu/2002-03/Tekstialgoritmid_I/Articles/Approximate/Hall_Dowling_Approximate_Matching_Review_1980_p381-hall.pdf

Approximate String Matching

With the increase in word and text processing computer systems, programs which check and correct spelling will become more and more common. Peterson investigates the basic structure of several such existing programs and their approaches to solving the problems which arise when this type of program is created. The basic framework and background necessary to write a spelling checker or corrector are provided.

Computer programs for detecting and correcting spelling errors

A model based on strikingly different philosophical as. sumptions from those currently popular is proposed for the design of online subject catalog access. Three design principles are presented and discussed: uncertainty (subject indexing is indeterminate and probabilistic beyond a certain point), variety (by Ashby’s law of requisite variety, variety of searcher query must equal variety of document indexing), and complexity (the search process, particularly during the entry and orientation phases, is subtler and more complex, on several grounds, than current models assume). Design features presented are an access phase, including entry and orientation, a hunting phase, and a selection phase. An end-user thesaurus and a front-end system mind are presented as examples of online catalog system components to improve searcher success during entry and orientation. The proposed model is “wrapped around” existing Library of Congress subject-heading indexing in such a way as to enhance access greatly without requiring reindexing. It is argued that both for cost reasons and in principle this is a superior approach to other design philosophies.

Subject access in online catalogs: A design model

This paper selectively surveys contributions to major topics in pattern recognition since 1968. Representative books and surveys pattern recognition published during this period are listed. Theoretical models for automatic pattern recognition are contrasted with practical,, design methodology. Research contributions to statistical and structural pattern recognition are selectively discussed, including contributions to error estimation and the experimental design of pattern classifiers. The survey concludes with a representative set of applications of pattern recognition technology.

Patterns in pattern recognition: 1968-1974

The effectiveness of various forms of contextual information in a postprocessing system for detection and correction of errors in words is examined. Various algorithms utilizing context are considered, from a dictionary algorithm which has available the maximum amount of information, to a set of contextual algorithms utilizing positional binary n-gram statistics. The latter information differs from the usual n-gram letter statistics in that the probabilities are position-dependent and each is quantized to 1 or 0, depending upon whether or not it is nonzero. This type of information is extremely compact and the computation for error correction is orders of magnitude less than that required by the dictionary algorithm.

A Contextual Postprocessing System for Error Correction Using Binary n-Grams

The static and dynamic utilization of a set of machine op-codes is examined. Two measures of the effective use of machine instructions are discussed and applied to samples of hand-coded programs and object code.

Measures of Op-Code Utilization

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A Contextual Postprocessing System for Error Correction Using Binary n-Grams

Measures of Op-Code Utilization