Contextual spelling errors are defined as the use of an incorrect, though valid, word in a particular sentence or context. Traditional spelling checkers flag misspelled words, but they do not typically attempt to identify words that are used incorrectly in a sentence. We explore the use of Latent Semantic Analysis for correcting these incorrectly used words and the results are compared to earlier work based on a Bayesian classifier. Spelling checkers are now available for all major word processing systems. However, these spelling checkers only catch errors that result in misspelled words. If an error results in a different, but incorrect word, it will go undetected. For example, quite may easily be mistyped as quiet. Another type of error occurs when a writer simply doesn't know which word of a set of homophones 1 (or near homophones) is the proper one for a particular context. For example, the usage of affect and effect is commonly confused. Though the cause is different for the two types of errors, we can treat them similarly by examining the contexts in which they appear. Consequently, no effort is made to distinguish between the two error types and both are called contextual spelling errors. Kukich (1992a; 1992b) reports that 40% to 45% of observed spelling errors are contextual errors. Sets of words which are frequently misused or mistyped for one another are identified as confusion sets. Thus, from our earlier examples, { quiet, quite} and { affect, effect} are two separate confusion sets. In this paper, we introduce Latent Semantic Analysis (LSA) as a method for correcting contextual spelling errors for a given collection of confusion sets.
REF made use of the semantic similarity produced by Latent Semantic Analysis.
Contextual Spelling Correction Using Latent Semantic Analysis
Applied Natural Language Processing Conference
Computer Science