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Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis

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  • Marcelo A Montemurro
  • Damián H Zanette

Abstract

The Voynich manuscript has remained so far as a mystery for linguists and cryptologists. While the text written on medieval parchment -using an unknown script system- shows basic statistical patterns that bear resemblance to those from real languages, there are features that suggested to some researches that the manuscript was a forgery intended as a hoax. Here we analyse the long-range structure of the manuscript using methods from information theory. We show that the Voynich manuscript presents a complex organization in the distribution of words that is compatible with those found in real language sequences. We are also able to extract some of the most significant semantic word-networks in the text. These results together with some previously known statistical features of the Voynich manuscript, give support to the presence of a genuine message inside the book.

Suggested Citation

  • Marcelo A Montemurro & Damián H Zanette, 2013. "Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
  • Handle: RePEc:plo:pone00:0066344
    DOI: 10.1371/journal.pone.0066344
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    References listed on IDEAS

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    1. J. P. Herrera & P. A. Pury, 2008. "Statistical keyword detection in literary corpora," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(1), pages 135-146, May.
    2. Stephen P. Harter, 1975. "A probabilistic approach to automatic keyword indexing. Part I. On the Distribution of Specialty Words in a Technical Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 26(4), pages 197-206, July.
    3. Ramon Ferrer-i-Cancho & Brita Elvevåg, 2010. "Random Texts Do Not Exhibit the Real Zipf's Law-Like Rank Distribution," PLOS ONE, Public Library of Science, vol. 5(3), pages 1-10, March.
    4. Marcelo A. Montemurro & Damián H. Zanette, 2010. "Towards The Quantification Of The Semantic Information Encoded In Written Language," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 135-153.
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    Cited by:

    1. Vladimír Matlach & Barbora Anna Janečková & Daniel Dostál, 2022. "The Voynich manuscript: Symbol roles revisited," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-26, January.
    2. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
    4. Choudhury, Nazim & Faisal, Fahim & Khushi, Matloob, 2020. "Mining Temporal Evolution of Knowledge Graphs and Genealogical Features for Literature-based Discovery Prediction," Journal of Informetrics, Elsevier, vol. 14(3).
    5. Espitia, Diego & Larralde, Hernán, 2020. "Universal and non-universal text statistics: Clustering coefficient for language identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    6. Quispe, Laura V.C. & Tohalino, Jorge A.V. & Amancio, Diego R., 2021. "Using virtual edges to improve the discriminability of co-occurrence text networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).

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