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Long-range correlations between letters and sentences in texts

Author

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  • Ebeling, Werner
  • Neiman, Alexander

Abstract

We mapped three long texts to random walks and calculated several correlation measures as Hölder exponents, higher-order cumulants and power spectra. By means of computer experiments we have found that shuffling on/or below the sentence level generates strings showing no anomalous diffusion, no higher-order cumulants and no power spectra with 1/fδ-shape. In this way we have shown that the long correlations reflected in these measures are not based on correlations inside sentences but reflect the large-scale structure beyond the sentence level.

Suggested Citation

  • Ebeling, Werner & Neiman, Alexander, 1995. "Long-range correlations between letters and sentences in texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 215(3), pages 233-241.
    • Handle: RePEc:eee:phsmap:v:215:y:1995:i:3:p:233-241
    DOI: 10.1016/0378-4371(95)00025-3
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    Citations

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    Cited by:

    1. Edoardo Magnone, 2014. "A novel graphical representation of sentence complexity: the description and its application," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1301-1329, February.
    2. Vieira, Denner S. & Picoli, Sergio & Mendes, Renio S., 2018. "Robustness of sentence length measures in written texts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 749-754.
    3. Kumiko Tanaka-Ishii & Armin Bunde, 2016. "Long-Range Memory in Literary Texts: On the Universal Clustering of the Rare Words," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-14, November.
    4. Heng Chen & Haitao Liu, 2018. "Quantifying Evolution of Short and Long-Range Correlations in Chinese Narrative Texts across 2000 Years," Complexity, Hindawi, vol. 2018, pages 1-12, February.
    5. Frank Emmert-Streib, 2013. "Structural Properties and Complexity of a New Network Class: Collatz Step Graphs," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-14, February.
    6. Shan Li & Ruokuang Lin & Chunhua Bian & Qianli D Y Ma & Plamen Ch Ivanov, 2016. "Model of the Dynamic Construction Process of Texts and Scaling Laws of Words Organization in Language Systems," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.
    7. Liu, Yang & Zhuo, Xuru & Zhou, Xiaozhu, 2024. "Multifractal analysis of Chinese literary and web novels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    8. Lambiotte, R. & Ausloos, M. & Thelwall, M., 2007. "Word statistics in Blogs and RSS feeds: Towards empirical universal evidence," Journal of Informetrics, Elsevier, vol. 1(4), pages 277-286.
    9. 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).
    10. Shuntaro Takahashi & Kumiko Tanaka-Ishii, 2017. "Do neural nets learn statistical laws behind natural language?," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-17, December.
    11. Ausloos, M., 2012. "Measuring complexity with multifractals in texts. Translation effects," Chaos, Solitons & Fractals, Elsevier, vol. 45(11), pages 1349-1357.

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