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- W1764051130 abstract "In this paper we report the results of a pi- lot study of basing readability prediction on training data annotated with reading time. Although reading time is known to be a good metric for predicting readabil- ity, previous work has mainly focused on annotating the training data with subjec- tive readability scores usually on a 1 to 5 scale. Instead of the subjective assess- ments of complexity, we use the more ob- jective measure of reading time. We create and evaluate a predictor using the binary classification problem; the predictor iden- tifies the better of two documents correctly with 68.55% accuracy. We also report a comparison of predictors based on reading time and on readability scores." @default.
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- W1764051130 date "2013-08-01" @default.
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- W1764051130 title "A Pilot Study of Readability Prediction with Reading Time" @default.
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