Constructing a Novel Chinese Readability Classification Model Using Principal Component Analysis and Genetic Programming

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@InProceedings{Lee:2012:ICALT,

• author = "Yi-Shian Lee and Hou-Chiang Tseng and Ju-Ling Chen and Chun-Yi Peng and Tao-Hsing Chang and Yao-Ting Sung",
• booktitle = "12th IEEE International Conference on Advanced Learning Technologies (ICALT 2012)",
• title = "Constructing a Novel Chinese Readability Classification Model Using Principal Component Analysis and Genetic Programming",
• year = "2012",
• pages = "164--166",
• keywords = "genetic algorithms, genetic programming, natural language processing, pattern classification, principal component analysis, text analysis, English text, Flesch-Kincaid formula, GP, PCA, multiple linguistic features, novel Chinese readability classification model, principal component analysis, text classification, text readability, Educational institutions, Mathematical model, Predictive models, Principal component analysis, Psychology, Support vector machines, Principal component analysis, Readability, Text analysis component",
• DOI = "doi:10.1109/ICALT.2012.134",
• abstract = "The 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.",
• notes = "Also known as \cite{6268065}",

}

Genetic Programming entries for Yi-Shian Lee Hou-Chiang Tseng Ju-Ling Chen Chun-Yi Peng Tao-Hsing Chang Yao-Ting Sung

Citations