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

研究成果: 書貢獻/報告類型會議貢獻

4 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
頁面164-166
頁數3
DOIs
出版狀態已發佈 - 2012 十月 8
事件12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 - Rome, 意大利
持續時間: 2012 七月 42012 七月 6

出版系列

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

其他

其他12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
國家意大利
城市Rome
期間12/7/412/7/6

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Education

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  • 引用此

    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. 於 Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 (頁 164-166). [6268065] (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). https://doi.org/10.1109/ICALT.2012.134