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Statistical metrics for languages classification: A case study of the Bible translations

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  • Mehri, Ali
  • Jamaati, Maryam

Abstract

Automatic language classification is an important contribution to linguistic research. Four statistical features concerning long-range correlations are applied to classify syntactic properties of languages. We calculate Zipf’s exponent, Heaps’ exponent, fractal dimension and entropy, for the Bible translations to one hundred live languages from twenty-eight language families. The Bible has unique concept regardless of its language, but the discrepancy in grammatical rules of the languages leads to difference in extracted measures from its various translations. The results show that, geographical distance and cultural differences can lead to statistical discrepancies. All extracted features for the Bible translations have normal distribution around their average value. This fact categorizes the languages into two groups; a majority of normal languages and a minority of abnormal ones. There is also evident (anti)correlation relation between each pair of the mentioned metrics due to their respective mechanism. Standard deviation of the considered statistical features over language families is affected by geographical distance between communities that speak to their languages and their cultural diversity.

Suggested Citation

  • Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:chsofr:v:144:y:2021:i:c:s0960077921000321
    DOI: 10.1016/j.chaos.2021.110679
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    References listed on IDEAS

    as
    1. Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.
    2. Ted Briscoe, 2008. "Language learning, power laws, and sexual selection," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 7(1), pages 65-76, June.
    3. Mehri, Ali & Darooneh, Amir H., 2011. "The role of entropy in word ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(18), pages 3157-3163.
    4. Ali Mehri & Sahar Mohammadpour Lashkari, 2016. "Power-law regularities in human language," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(11), pages 1-6, November.
    5. Gamallo, Pablo & Pichel, José Ramom & Alegria, Iñaki, 2017. "From language identification to language distance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 152-162.
    6. Mehri, Ali & Agahi, Hamzeh & Mehri-Dehnavi, Hossein, 2019. "A novel word ranking method based on distorted entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 484-492.
    7. Petroni, Filippo & Serva, Maurizio, 2010. "Measures of lexical distance between languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(11), pages 2280-2283.
    8. Mehri, Ali & Darooneh, Amir H. & Shariati, Ashrafalsadat, 2012. "The complex networks approach for authorship attribution of books," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2429-2437.
    9. Gao, Yuyang & Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Comparison of directed and weighted co-occurrence networks of six languages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 579-589.
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