IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v7y2013i2p80-96.html
   My bibliography  Save this article

Measurement of Textual Complexity Based on Categorical Invariance

Author

Listed:
  • Lixiao Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China & Shanghai Sanda University, Shanghai, China)

  • Jun Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

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.

Suggested Citation

  • Lixiao Zhang & Jun Zhang, 2013. "Measurement of Textual Complexity Based on Categorical Invariance," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 7(2), pages 80-96, April.
    • Handle: RePEc:igg:jcini0:v:7:y:2013:i:2:p:80-96
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijcini.2013040106
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:7:y:2013:i:2:p:80-96. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.