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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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