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    An intelligent spelling error correction system based on the results of an analysis which has established a set of phonological and sequential rules obeyed by misspellings.

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    Publication date
    2009-10-28T15:54:42Z
    Author
    Fawthrop, David
    Supervisor
    Yannakoudakis, E. J.
    Keyword
    Spelling errors
    Typing errors
    Empirical rules
    Spelling error correction algorithm
    Expert system
    Rights
    Intellectual property rights to the programs and concepts developed here belong to D. Fawthrop and JLEVITAL Ltd. They may not be used for commercial purposes without the written permission of the owner of the rights. Use of these programs or concepts for research will be allowed subject to specific written agreement with D. Fawthrop and JLEVITAL Ltd.
    Institution
    University of Bradford
    Department
    Postgraduate School of Studies in Computing
    Awarded
    1984
    
    Metadata
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    Abstract
    This thesis describes the analysis of over 1300 spelling and typing errors. It introduces and describes many empirical rules which these errors obey and shows that a vast majority of errors are variations on some 3000 basic forms. It also describes and tests an intelligent, knowledge based spelling error correction algorithm based on the above work. Using the Shorter Oxford English dictionary it correctly identifies over 90% of typical spelling errors and over 80% of all spelling errors, where the correct word is in the dictionary. The methodology used is as follows: An error form is compared with each word in that small portion of the dictionary likely to contain the intended word, but examination of improbable words is rapidly abandoned using heuristic rules. Any differences between the dictionary word and the error form are compared with the basic forms. Any dictionary word which differs from the error form only by one or two basic forms is transferred to a separate list. The program then acts as an expert system where each of the basic forms is a production or rule with a subjective Bayesian probability. A choice is made from the list by calculating the Bayesian probability for each word in the separate list. An interactive spelling error corrector using the concepts and methods developed here is operating on the Bradford University Cyber 170/720 Computer, and was used to correct this thesis. The corrector also runs on VAX and Prime computers.
    URI
    http://hdl.handle.net/10454/3795
    Type
    Thesis
    Qualification name
    PhD
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    Theses

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