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Computer spellcheckers work fine unless you accidentally type in a correctly
spelled word that is incorrect in meaning, such as keying in “sight” instead of
“site”. But now Klaus Truemper of the University of Texas in Dallas has
developed a spellchecker that can weed out these errors by learning how the user
composes text. Like early voice-recognition systems, it uses statistical
analysis to try and decide what the text should mean and whether the correct
words have been used. Early tests show the system suggests the correct word for
an error 96 per cent of the time.