國立臺灣師範大學教育心理與輔導學系Lee, Y. S.Tseng, H. C.Chen, J. L.Peng, C. Y.Chang, T. H.Sung, Y. T.2014-12-022014-12-022012-07-06http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/40789The studies of readability aim to measure the level of text difficulty. Although traditional formulae such as the Flesch-Kincaid formula can properly predict text readability, they are only effective for English text. Other formulae with very few features may result in inaccurate text classification. The study takes into account multiple linguistic features, and attempts to increase the level of accuracy in text classification by adopting a new model which integrates Principal Component Analysis (PCA) with Genetic Programming (GP). Empirical data are used to demonstrate the performance of the proposed model.genetic algorithmsgenetic programmingnatural language processingpattern classificationprincipal component analysistext analysisEnglish textFlesch-Kincaid formulaGPPCAmultiple linguistic featuresnovel Chinese readability classification modelprincipal component analysistext classificationtext readabilityEducational institutionsMathematical modelPredictive modelsPrincipal component analysisPsychologySupport vector machinesPrincipal component analysisReadabilityText analysis component