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The Small-World of Human Language

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  • Ramon Ferrer i Cancho
  • Ricard V. Solé

Abstract

Words in human language interact within sentences in non-random ways, and allow humans to construct an astronomic variety of sentences from a limited number of discrete units. This construction process is extremely fast and robust. The coocurrence of words within sentences reflect language organization in a subttle manner which can be described in terms of a graph of word interactions. Here we show that such graph displays two important features recently found in a disparate number of complex systems: (a) The so called small world effect. In particular, the average distance between two words d (i.e. the average minimum number of jumps to be made from an arbitrary word to another) is shown to be d \approx 2-3, in spite that the human brain can store many thousands. (b) A scale-free distribution of degrees. The known dramatic effects of disconnecting the most connected vertices in such networks can be identified in some language disorders. These observations suggest some unexpected features of language organization that might reflect the evolutionary and social history of lexicons and the origins of their flexibility and combinatorial nature.

Suggested Citation

  • Ramon Ferrer i Cancho & Ricard V. Solé, 2001. "The Small-World of Human Language," Working Papers 01-03-016, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:01-03-016
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    References listed on IDEAS

    as
    1. Jose M. Montoya & Ricard V. Solé, 2000. "Small World Patterns in Food Webs," Working Papers 00-10-059, Santa Fe Institute.
    2. Ricard V. Solé & José M. Montoya, 2000. "Complexity and Fragility in Ecological Networks," Working Papers 00-11-060, Santa Fe Institute.
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    Cited by:

    1. Xiao, Wenjun & Liu, Yanxia & Chen, Guanrong, 2014. "Characterizing vertex-degree sequences in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 291-295.
    2. Li, Jianyu & Zhou, Jie, 2007. "Chinese character structure analysis based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 629-638.
    3. Tsonis, A.A. & Roebber, P.J., 2004. "The architecture of the climate network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 497-504.
    4. 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.
    5. repec:eee:phsmap:v:487:y:2017:i:c:p:103-110 is not listed on IDEAS
    6. Xiao, Wenjun & Lin, Longxin & Chen, Guanrong, 2015. "Vertex-degree sequences in complex networks: New characteristics and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 437-441.
    7. Sheng, Long & Li, Chunguang, 2009. "English and Chinese languages as weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2561-2570.
    8. STANKOVA, Marija & MARTENS, David & PROVOST, Foster, 2015. "Classification over bipartite graphs through projection," Working Papers 2015001, University of Antwerp, Faculty of Applied Economics.

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