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

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 utilized to demonstrate the performance of the proposed model. KeywordsReadability; Principal component analysis; Genetic programming; Text analysis component

DOI: 10.1109/ICALT.2012.134

Cite this paper

@inproceedings{Lee2012ConstructingAN, title={Constructing a Novel Chinese Readability Classification Model Using Principal Component Analysis and Genetic Programming}, 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={ICALT}, year={2012} }