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Universal Entropy of Word Ordering Across Linguistic Families

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  • Marcelo A Montemurro
  • Damián H Zanette

Abstract

Background: The language faculty is probably the most distinctive feature of our species, and endows us with a unique ability to exchange highly structured information. In written language, information is encoded by the concatenation of basic symbols under grammatical and semantic constraints. As is also the case in other natural information carriers, the resulting symbolic sequences show a delicate balance between order and disorder. That balance is determined by the interplay between the diversity of symbols and by their specific ordering in the sequences. Here we used entropy to quantify the contribution of different organizational levels to the overall statistical structure of language. Methodology/Principal Findings: We computed a relative entropy measure to quantify the degree of ordering in word sequences from languages belonging to several linguistic families. While a direct estimation of the overall entropy of language yielded values that varied for the different families considered, the relative entropy quantifying word ordering presented an almost constant value for all those families. Conclusions/Significance: Our results indicate that despite the differences in the structure and vocabulary of the languages analyzed, the impact of word ordering in the structure of language is a statistical linguistic universal.

Suggested Citation

  • Marcelo A Montemurro & Damián H Zanette, 2011. "Universal Entropy of Word Ordering Across Linguistic Families," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-9, May.
  • Handle: RePEc:plo:pone00:0019875
    DOI: 10.1371/journal.pone.0019875
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    Cited by:

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    2. Louise Bogéa Ribeiro & Anderson Raiol Rodrigues & Kauê Machado Costa & Manoel da Silva Filho, 2019. "Quantification of textual comprehension difficulty with an information theory-based algorithm," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-9, December.
    3. Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    4. Carlos F Alvarez & Luis E Palafox & Leocundo Aguilar & Mauricio A Sanchez & Luis G Martinez, 2016. "Using Link Disconnection Entropy Disorder to Detect Fast Moving Nodes in MANETs," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.
    5. Jingxian Liao & Guowei Yang & David Kavaler & Vladimir Filkov & Prem Devanbu, 2019. "Status, identity, and language: A study of issue discussions in GitHub," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-20, June.

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