Constructing a novel Chinese readability classification model using principal component analysis and genetic programming

Yi Shian Lee, Hou Chiang Tseng, Ju Ling Chen, Chun Yi Peng, Tao Hsing Chang, Yao-Ting Sung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
Pages164-166
Number of pages3
DOIs
Publication statusPublished - 2012 Oct 8
Event12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 - Rome, Italy
Duration: 2012 Jul 42012 Jul 6

Publication series

NameProceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012

Other

Other12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
CountryItaly
CityRome
Period12/7/412/7/6

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Keywords

  • Genetic programming
  • Principal component analysis
  • Readability
  • Text analysis component

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Education

Cite this

Lee, Y. S., Tseng, H. C., Chen, J. L., Peng, C. Y., Chang, T. H., & Sung, Y-T. (2012). Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. In Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 (pp. 164-166). [6268065] (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). https://doi.org/10.1109/ICALT.2012.134