IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i11p1937-d439171.html
   My bibliography  Save this article

A Short-Patterning of the Texts Attributed to Al Ghazali: A “Twitter Look” at the Problem

Author

Listed:
  • Zeev Volkovich

    (Software Engineering Department, ORT Braude College of Engineering, Karmiel 21982, Israel)

Abstract

This article presents an novel approach inspired by the modern exploration of short texts’ patterning to creations prescribed to the outstanding Islamic jurist, theologian, and mystical thinker Abu Hamid Al Ghazali. We treat the task with the general authorship attribution problematics and employ a Convolutional Neural Network (CNN), intended in combination with a balancing procedure to recognize short, concise templates in manuscripts. The proposed system suggests new attitudes make it possible to investigate medieval Arabic documents from a novel computational perspective. An evaluation of the results on a previously tagged collection of books ascribed to Al Ghazali demonstrates the method’s high reliability in recognizing the source authorship. Evaluations of two famous manuscripts, Mishakat al-Anwa and Tahafut al-Falasifa , questioningly attributed to Al Ghazali or co-authored by him, exhibit a significant difference in their overall stylistic style with one inherently assigned to Al Ghazali. This fact can serve as a substantial formal argument in the long-standing dispute about these manuscripts’ authorship. The proposed methodology suggests a new look on the perusal of medieval documents’ inner structures and possible authorship from the short-patterning and signal processing perspectives.

Suggested Citation

  • Zeev Volkovich, 2020. "A Short-Patterning of the Texts Attributed to Al Ghazali: A “Twitter Look” at the Problem," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:1937-:d:439171
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/11/1937/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/11/1937/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moshe Koppel & Jonathan Schler & Shlomo Argamon, 2009. "Computational methods in authorship attribution," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 9-26, January.
    2. Aydoğan, Murat & Karci, Ali, 2020. "Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jaroslav Ráček & Jan Ministr, 2014. "Tools for Automatic Recognition of Persons and their Relationships in Unstructured Data [Nástroje pro automatické rozpoznávání entit a jejich vztahů v nestrukturovaných textech]," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2014(3), pages 280-287.
    2. Matthew J. Schneider & Shawn Mankad, 2021. "A Two-Stage Authorship Attribution Method Using Text and Structured Data for De-Anonymizing User-Generated Content," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 8(3), pages 66-83, September.
    3. Kargin, Vladislav, 2016. "On variation of word frequencies in Russian literary texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 328-334.
    4. Mingfang Wu & David Hawking & Andrew Turpin & Falk Scholer, 2012. "Using anchor text for homepage and topic distillation search tasks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(6), pages 1235-1255, June.
    5. Haoran Zhu & Lei Lei, 2022. "The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis," SAGE Open, , vol. 12(2), pages 21582440221, April.
    6. Mike Thelwall, 2017. "Avoiding obscure topics and generalising findings produces higher impact research," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 307-320, January.
    7. Chunneng Huang & Tianjun Fu & Hsinchun Chen, 2010. "Text‐based video content classification for online video‐sharing sites," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(5), pages 891-906, May.
    8. Silvia Corbara & Alejandro Moreo & Fabrizio Sebastiani, 2023. "Syllabic quantity patterns as rhythmic features for Latin authorship attribution," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 128-141, January.
    9. Song, Min & Kim, Erin Hea-Jin & Kim, Ha Jin, 2015. "Exploring author name disambiguation on PubMed-scale," Journal of Informetrics, Elsevier, vol. 9(4), pages 924-941.
    10. Ankita Dhar & Himadri Mukherjee & Shibaprasad Sen & Md Obaidullah Sk & Amitabha Biswas & Teresa Gonçalves & Kaushik Roy, 2022. "Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents," Future Internet, MDPI, vol. 14(10), pages 1-20, September.
    11. de Arruda, Henrique F. & Marinho, Vanessa Q. & Lima, Thales S. & Amancio, Diego R. & Costa, Luciano da F., 2018. "An image analysis approach to text analytics based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 110-120.
    12. Stefano Sbalchiero & Maria Stella Righettini, 2017. "Rhetorical manifestation of institutional transformation," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1279-1296, May.
    13. Refat Aljumily, 2015. "Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to “Shakespeare Authorship Question”," Social Sciences, MDPI, vol. 4(3), pages 1-42, September.
    14. Gordon J. Ross, 2020. "Tracking the evolution of literary style via Dirichlet–multinomial change point regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 149-167, January.

    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:gam:jmathe:v:8:y:2020:i:11:p:1937-:d:439171. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.