Mind & Society

, Volume 7, Issue 1, pp 65–76 | Cite as

Language learning, power laws, and sexual selection

Original Article

Abstract

I discuss the ubiquity of power law distributions in language organisation (and elsewhere), and argue against Miller’s (The mating mind: How sexual choice shaped the evolution of human nature, William Heinemann, London, 2000) argument that large vocabulary size is a consequence of sexual selection. Instead I argue that power law distributions are evidence that languages are best modelled as dynamical systems but raise some issues for models of iterated language learning.

Keywords

Zipf curve Iterated learning model Small world distribution Evolutionary linguistics Diathesis alternation 

Notes

Acknowledgments

I would like to thank the anonymous reviewers for their helpful comments, and Paula Buttery and Anna Korhonen for analysis and plots of data from the Valex lexicon.

References

  1. Albert R, Barabasi A (2002) Statistical machanics of complex networks. Rev Mod Phys 74:47–97CrossRefGoogle Scholar
  2. Baayen H (1991) A stochastic process for word frequency distributions. In: Proceedings of the assoc for computational linguistics, Morgan Kaufmann, Menlo Park, CA, pp 271–278Google Scholar
  3. Baayen H (2001) Word frequency distributions. Kluwer, DordrechtGoogle Scholar
  4. Bak P (1996) How nature works: the sicence of self-organized criticality. Copernicus Press, New YorkGoogle Scholar
  5. Bornholdt S, Ebel H (2001) World Wide Web scaling exponent from Simons 1955 model. Phys Rev 64:035104CrossRefGoogle Scholar
  6. Briscoe EJ (2000) Evolutionary perspectives on diachronic syntax. In: Pintzuk S, Tsoulas G, Warner A (eds) Diachronic syntax: models and mechanisms. Oxford University Press, Oxford, pp 75–108Google Scholar
  7. Briscoe EJ, Copestake AA, Lascarides A (1995) Blocking. In: Dizier P St, Viegas E (eds) Computational lexical semantics. Cambridge University Press, Cambridge, pp 273–302Google Scholar
  8. Buttery P, Korhonen A (2005) Large-scale analysis of verb subcategorization differences between child directed speech and adult speech. In: Proceedings of the interdisciplinary workshop on the identification and representation of verb features and verb classes, Saarland UniversityGoogle Scholar
  9. Church K, Gale W (1995) Poisson mixtures. Nat Lang Eng 1:1–36CrossRefGoogle Scholar
  10. Clark E (2003) First language acquisition. Cambridge University Press, CambridgeGoogle Scholar
  11. Conwell E, Demuth K (2007) Early syntactic productivity: evidence from dative shift. Cognition 103:163–179CrossRefGoogle Scholar
  12. Copestake AA, Briscoe EJ (1995) Regular polysemy and semi-productive sense extension. J Semant 12:15–67CrossRefGoogle Scholar
  13. Diamond J (1997) Guns, germs and steel: the fate of human societies. Random House, New YorkGoogle Scholar
  14. Ferrer i Cancho R, Sole R (2001) The small world of human language. Proc R Soc B Biol Sci 268(1482):2261–2265CrossRefGoogle Scholar
  15. Guiraud H (1954) Les Characteres Statistiques du Vocabulaire. Press Universitaires de France, ParisGoogle Scholar
  16. Kirby S (2001) Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity. IEEE Trans Evol Comput 5(2):102–110CrossRefGoogle Scholar
  17. Korhonen A (2002) Subcategorization acquisition. Computer Laboratory, University of Cambridge, Techical Report UCAM-CL-TR-530Google Scholar
  18. Korhonen A, Krymolowski Y, Briscoe EJ (2006) A large subcategorization lexicon for natural language processing applications. In: Proceedings of the 5th international conference on language resources and evaluation (LREC06), Genova, ItalyGoogle Scholar
  19. Mandelbrot B (1953) An informational theory of the statistical structure of language. In: Jackson W (ed) Communication theory. Butterworths, LondonGoogle Scholar
  20. Manning C, Schutze H (1999) Foundations of statistical natural language processing. MIT Press, CambridgeGoogle Scholar
  21. Miller G (2000) The mating mind: how sexual choice shaped the evolution of human nature. William Heinemann, LondonGoogle Scholar
  22. Preiss J, Korhonen A, Briscoe EJ (2002) Subcategorization acquisition as an evaluation method for WSD. In: Proceedings of the language resources and evaluation conference (LREC02), Morgan Kaufmann, Menlo Park, CA, pp 1551–1556Google Scholar
  23. Sampson G (2001) Empirical linguistics. Continuum, LondonGoogle Scholar
  24. Schulze C, Stauffer D (2006) Recent developments in computer simulations of language competition. Comput Sci Eng 8:86–93CrossRefGoogle Scholar
  25. Sharman R (1989) Observational evidence for a statistical model of language, IBM UKSC Report 205Google Scholar
  26. Simon H (1955) On a class of skew distribution functions. Biometrika 42:435–440Google Scholar
  27. Wichmann S (2005) On the power law distribution of language family sizes. J Linguist 41:117–131CrossRefGoogle Scholar
  28. Yook S, Jeong H, Barabasi A-L, Tu Y (2001) Weighted evolving networks. Phys Rev Lett 86:5835–5838CrossRefGoogle Scholar
  29. Zipf G (1935) The psycho-biology of language: an introduction to dynamic philology. Houghton-Miflin, New YorkGoogle Scholar
  30. Zipf G (1949) Human behavior and the principle of least effort. Addison-Wesley, CambridgeGoogle Scholar

Copyright information

© Fondazione Rosselli 2007

Authors and Affiliations

  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK

Personalised recommendations