IDEAS home Printed from https://ideas.repec.org/a/hin/complx/9362468.html
   My bibliography  Save this article

Quantifying Evolution of Short and Long-Range Correlations in Chinese Narrative Texts across 2000 Years

Author

Listed:
  • Heng Chen
  • Haitao Liu

Abstract

We investigate how short and long-range word length correlations evolve in Chinese narrative texts. The results show that, for short-range word length correlations, no significant linear evolutionary trend was found. But for long-range correlations, there are two opposite tendencies for two different regimes: the Hurst exponent of small-scale (box size ranges from 10 to 100) word length correlations decreases over time, and the exponent of large-scale (box size ranges from 101 to 1000) shows an increasing tendency. The increase of word length is corroborated as an essential regularity of word evolution in written Chinese. Further analyses show that a significant correlation coefficient is obtained between Hurst exponents from the small-scale correlations and mean word length across time. These indicate that word length correlation evolution possesses different self-adaptive mechanisms in terms of different scales of distances between words. We speculate that the increase of word length and sentence length in written Chinese may account for this phenomenon, in terms of both the social-cultural aspects and the self-adapting properties of language structures.

Suggested Citation

  • 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.
  • Handle: RePEc:hin:complx:9362468
    DOI: 10.1155/2018/9362468
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/9362468.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/8503/2018/9362468.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2018/9362468?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Heng Chen & Junying Liang & Haitao Liu, 2015. "How Does Word Length Evolve in Written Chinese?," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.
    2. Eduardo G Altmann & Janet B Pierrehumbert & Adilson E Motter, 2009. "Beyond Word Frequency: Bursts, Lulls, and Scaling in the Temporal Distributions of Words," PLOS ONE, Public Library of Science, vol. 4(11), pages 1-7, November.
    3. Tianguang Yang & Changgui Gu & Huijie Yang, 2016. "Long-Range Correlations in Sentence Series from A Story of the Stone," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-11, September.
    4. 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.
    5. 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.
    6. Bhan, Jaemi & Kim, Sowoon & Kim, Jongkwang & Kwon, Younghun & Yang, Sung-il & Lee, Kunsang, 2006. "Long-range correlations in Korean literary corpora," Chaos, Solitons & Fractals, Elsevier, vol. 29(1), pages 69-81.
    7. Şahin, Gökhan & Erentürk, Murat & Hacinliyan, Avadis, 2009. "Detrended fluctuation analysis in natural languages using non-corpus parametrization," Chaos, Solitons & Fractals, Elsevier, vol. 41(1), pages 198-205.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. 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.
    2. Yue Yang & Changgui Gu & Qin Xiao & Huijie Yang, 2017. "Evolution of scaling behaviors embedded in sentence series from A Story of the Stone," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-14, February.
    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. Yuan, Qianshun & Semba, Sherehe & Zhang, Jing & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2021. "Multi-scale transition matrix approach to time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    5. Ghosh, Dipak & Chakraborty, Sayantan & Samanta, Shukla, 2019. "Study of translational effect in Tagore’s Gitanjali using Chaos based Multifractal analysis technique," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1343-1354.
    6. 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).
    7. 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.
    8. 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).
    9. Eduardo G Altmann & Janet B Pierrehumbert & Adilson E Motter, 2011. "Niche as a Determinant of Word Fate in Online Groups," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    10. 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.
    11. Mehri, Ali & Jamaati, Maryam, 2021. "Statistical metrics for languages classification: A case study of the Bible translations," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    12. 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.
    13. 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.
    14. Chen, Yanguang, 2012. "Zipf’s law, 1/f noise, and fractal hierarchy," Chaos, Solitons & Fractals, Elsevier, vol. 45(1), pages 63-73.
    15. Zörnig, Peter, 2010. "Statistical simulation and the distribution of distances between identical elements in a random sequence," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2317-2327, October.
    16. Cui, Xue-Mei & Yoon, Chang No & Youn, Hyejin & Lee, Sang Hoon & Jung, Jean S. & Han, Seung Kee, 2017. "Dynamic burstiness of word-occurrence and network modularity in textbook systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 103-110.
    17. Karain, Wael I., 2019. "Investigating large-amplitude protein loop motions as extreme events using recurrence interval analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 1-10.
    18. 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.
    19. 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.
    20. Rashidisabet, Homa & Ajilore, Olusola & Leow, Alex & Demos, Alexander P., 2022. "Revisiting power-law estimation with applications to real-world human typing dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).

    More about this item

    Statistics

    Access and download statistics

    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:hin:complx:9362468. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.