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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.
Fawthrop, David
Fawthrop, David
Publication Date
2009-10-28T15:54:42Z
End of Embargo
Supervisor
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.
Peer-Reviewed
Open Access status
Accepted for publication
Institution
University of Bradford
Department
Postgraduate School of Studies in Computing
Awarded
1984
Embargo end date
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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.
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Type
Thesis
Qualification name
PhD