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

doi:10.1109/ICALT.2012.134

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 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 language	English
Title of host publication	Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
Pages	164-166
Number of pages	3
DOIs	https://doi.org/10.1109/ICALT.2012.134
Publication status	Published - 2012
Event	12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012 - Rome, Italy Duration: 2012 Jul 4 → 2012 Jul 6

Publication series

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

Other

Other	12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012
Country/Territory	Italy
City	Rome
Period	2012/07/04 → 2012/07/06

Keywords

Genetic programming
Principal component analysis
Readability
Text analysis component

ASJC Scopus subject areas

Computer Networks and Communications
Education

Access to Document

10.1109/ICALT.2012.134

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). Article 6268065 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). https://doi.org/10.1109/ICALT.2012.134

Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. / Lee, Yi Shian; Tseng, Hou Chiang; Chen, Ju Ling et al.
Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. 2012. p. 164-166 6268065 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, YS, Tseng, HC, Chen, JL, Peng, CY, Chang, TH & Sung, YT 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., 6268065, Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012, pp. 164-166, 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012, Rome, Italy, 2012/07/04. https://doi.org/10.1109/ICALT.2012.134

Lee YS, Tseng HC, Chen JL, Peng CY, Chang TH, Sung YT. 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. 2012. p. 164-166. 6268065. (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012). doi: 10.1109/ICALT.2012.134

Lee, Yi Shian ; Tseng, Hou Chiang ; Chen, Ju Ling et al. / Constructing a novel Chinese readability classification model using principal component analysis and genetic programming. Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012. 2012. pp. 164-166 (Proceedings of the 12th IEEE International Conference on Advanced Learning Technologies, ICALT 2012).

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title = "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.",

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AB - 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.

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

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Keywords

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