A Spelling Mistake Correction (SMC) Model for Resolving Real-Word Error

Abstract

Spelling correction has been haunting humans in various fields of society like while creating business proposals, contract tenders, students doing their assignments, in email communications, sending request for proposals, while writing content for website and so on. Already existing Dictionary based correction approaches have helped by providing solution to the problem when the written word doesn’t even qualify to be called as a legal word. But is it the only challenge a writer faces while writing the desired documents! The words, which fall in the category of correct spelling words, may sometimes be the word which writer did not intend to write. The above illustrated genre of errors is called Real-Word error. This paper proposes a spelling correction system whose main focus is on automatic identification and correction of real word errors accurately and efficiently. The approach includes hybridization of Trigram and Bayesian approach and using Modified Brown corpus as a training set. A large set of commonly confused words is used in this case for evaluating the performance of the proposed approach.