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Measurement of Textual Complexity Based on Categorical Invariance

Measurement of Textual Complexity Based on Categorical Invariance

Lixiao Zhang, Jun Zhang
Copyright: © 2013 |Volume: 7 |Issue: 2 |Pages: 17
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466632486|DOI: 10.4018/ijcini.2013040106
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MLA

Zhang, Lixiao, and Jun Zhang. "Measurement of Textual Complexity Based on Categorical Invariance." IJCINI vol.7, no.2 2013: pp.80-96. http://doi.org/10.4018/ijcini.2013040106

APA

Zhang, L. & Zhang, J. (2013). Measurement of Textual Complexity Based on Categorical Invariance. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(2), 80-96. http://doi.org/10.4018/ijcini.2013040106

Chicago

Zhang, Lixiao, and Jun Zhang. "Measurement of Textual Complexity Based on Categorical Invariance," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7, no.2: 80-96. http://doi.org/10.4018/ijcini.2013040106

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Abstract

Based on the categorical invariance in human concept learning a measurement of textual complexity is proposed. To reach this, transformations of keywords are defined. If a reader grasps the meaning of keywords and the semantic relationship between keywords and sentences, the authors say he/she has understood the text. The transformations of keywords take the difficulty of keywords and the semantic relations between keywords into account. If a text has more common keywords and relations, its complexity is lower. The experiment shows that the measurement is workable. Representational information based on text complexity is to measure the amount of the information in sentences in respect to the whole text. The example shows that the measured information of each sentence is in accordance with the reader’s reading experience.

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