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.

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.

<div>Typing in Japanese is a difficult process for novice and intermediate learners of Japanese due</div><div>to the writing system of the Japanese language and its comparatively involved input method on a</div><div>keyboard. Considering that spell checkers, which enable the user to check and correct their own</div><div>errors and select the correct kanji word, are designed for native speakers, the learners of Japanese</div><div>as a foreign language (JFL) may not recognize their spelling errors and are thus unable to selfcorrect using this built-in tool.</div><div>The present study addresses this problem and conducts an experiment to evaluate the</div><div>effectiveness of a visual feedback tool by its error recognition rate on the learners spelling errors</div><div>when typing in Japanese. The participants were 46 beginner level JFL learners in a third semester</div><div>Japanese course, and the majority consist of native speakers of Chinese or English. The</div><div>participants participated in two experimental sessions. In both sessions, participants were audio</div><div>recorded while reading aloud a list of words in Japanese for pronunciation analysis and screen</div><div>recorded while typing the same list of Japanese words. These recordings are used to analyze the</div><div>characteristics of error patterns in both pronunciation and typing. During the typing sessions, visual</div><div>feedback is provided to the participants via a customized dictionary tool when participants make</div><div>a spelling error.</div><div>The results show that regardless of the native language, the learners have difficulty on certain</div><div>words that include long vowels or double consonants. The recorded error patterns align with the</div><div>findings of previous studies (Hatasa, 2001; Nakazawa, 2003; Tsuchiya, 2000), and the visual</div><div>feedback showed an average error recognition rate of 76% of the participants’ spelling errors. The</div><div>participants also assessed the dictionary tool in terms of usability, and their responses indicate that</div><div>such tools are very useful during typing. The researcher concludes that using a visual feedback</div><div>dictionary tool is effective in recognizing the spelling errors of the learners when typing, and it</div><div>increases the learner’s awareness of spelling accuracy.</div>