Skip to main content

Investigating the Phonetic Organisation of the English Language via Phonological Networks, Percolation and Markov Models

  • Conference paper
  • First Online:
Proceedings of ECCS 2014

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

  • 694 Accesses

Abstract

Applying tools from network science and statistical mechanics, this paper represents an interdisciplinary analysis of the phonetic organisation of the English language. By using open datasets, we build phonological networks, where nodes are the phonetic pronunciations of words and edges connect words differing by the addition, deletion, or substitution of exactly one phoneme. We present an investigation of whether the topological features of this phonological network reflect only lower or also higher order correlations in phoneme organisation. We address this question by exploring artificially constructed repertoires of words, constructing phonological networks for these repertoires, and comparing them to the network constructed from the real data. Artificial repertoires of words are built to reflect increasingly higher order statistics of the English corpus. Hence, we start with percolation-type experiments in which phonemes are sampled uniformly at random to construct words, then sample from the real phoneme frequency distribution, and finally we consider repertoires resulting from Markov processes of first, second, and third order. As expected, we find that percolation-type experiments constitute a poor null model for the real data. However, some network features, such as the relatively high assortative mixing by degree and the clustering coefficient of the English PN, can be retrieved by Markov models for word construction. Nevertheless, even Markov processes up to third order cannot fully reproduce other patterns of the empirical network, such as link densities and component sizes. We conjecture that this difference is related to the combinatorial space the real and the artificial phonological networks are embedded into and that the connectivity properties of phonological networks reflect additional patterns in word organisation in the English language which cannot be captured by lower order phoneme correlations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    A malapropism is a type of word speech error where a target word is erroneously substituted by a phonologically similar word but from a different semantic context.

References

  1. Solé, R.V., Seoane, L.F.: Ambiguity in Language Networks. arXiv:1402.4802 (2014)

  2. Kello, C.T., Beltz, B.C.: Scale-free networks in phonological and orthographic wordform lexicons. In: Approaches to Phonological Complexity (2009)

    Google Scholar 

  3. Kintsch, W.: The role of knowledge in discourse comprehension: a construction-integration model. Psychol. Rev. 95(2), 163 (1988)

    Article  Google Scholar 

  4. Aitchison, J.: Words in the Mind: An Introduction to the Mental Lexicon. Wiley (2012)

    Google Scholar 

  5. Ferrer-i-Cancho, R., Solé, R.V.: The small world of human language. Proc. R. Soc. Lond. Series B: Biol. Sci. 268(1482), 2261–2265 (2001)

    Google Scholar 

  6. Baronchelli, A., Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N., Christiansen, M.H.: Networks in cognitive science. Trends Cogn. Sci. 17(7), 348–360 (2013)

    Article  Google Scholar 

  7. Arbesman, S., Strogatz, S.H., Vitevitch, M.S.: The structure of phonological networks across multiple languages. Int. J. Bifurcat. Chaos 20(03), 679–685 (2010)

    Article  Google Scholar 

  8. Chan, K.Y., Vitevitch, M.S.: Network structure influences speech production. Cogn. Sci. 34(4), 685–697 (2010)

    Article  Google Scholar 

  9. De Deyne, S., Storms, G.: Word associations: network and semantic properties. Behav. Res. Meth. 40(1), 213–231 (2008)

    Article  Google Scholar 

  10. Griffiths, T.L., Steyvers, M., Firl, A.: Google and the mind predicting fluency with pagerank. Psychol. Sci. 18(12), 1069–1076 (2007)

    Article  Google Scholar 

  11. Vitevitch, M.S., Chan, K.Y., Goldstein, R.: Insights into failed lexical retrieval from network science. Cogn. Psychol. 68, 1–32 (2014)

    Article  Google Scholar 

  12. Vitevitch, M.S.: What can graph theory tell us about word learning and lexical retrieval? J. Speech, Lang. Hear. Res. 51(2), 408–422 (2008)

    Article  Google Scholar 

  13. Vitevitch, M.S., Chan, K.Y., Roodenrys, S.: Complex network structure influences processing in long-term and short-term memory. J. Mem. Lang. 67(1), 30–44 (2012)

    Article  Google Scholar 

  14. Elman, L.J.: An alternative view of the mental lexicon. Trends Cogn. Sci. 8(7), 301–306 (2004)

    Article  Google Scholar 

  15. Ke, J.: Complex Networks and Human Language. arXiv preprint cs/0701135 (2007)

    Google Scholar 

  16. Siew, C.S.: Community structure in the phonological network. Front. Psychol. 4 (2013)

    Google Scholar 

  17. Luce, P.A., Pisoni, D.B.: Recognizing spoken words: the neighborhood activation model. Ear Hear. 19(1) (1998)

    Google Scholar 

  18. Sadat, J., Martin, C.D., Costa, A., Alario, F., et al.: Reconciling phonological neighborhood effects in speech production through single trial analysis. Cogn. Psychol. 68, 33–58 (2014)

    Article  Google Scholar 

  19. Vitevitch, M.S.: The neighborhood characteristics of malapropisms. Lang. Speech 40(3), 211–228 (1997)

    Google Scholar 

  20. Stella, M., Brede, M.: Patterns in the English language: phonological networks, percolation and assembly models. J. Stat. Mech. P05006 (2015)

    Google Scholar 

  21. Newman, M.: Networks: An Introduction. Oxford University Press (2010)

    Google Scholar 

  22. Gruenenfelder, T.M., Pisoni, D.B.: The lexical restructuring hypothesis and graph theoretic analyses of networks based on random lexicons. J. Speech, Lang. Hear. Res. 52(3), 596–609 (2009)

    Article  Google Scholar 

  23. Grimmett, G., Stirzaker, D.: Probability and Random Processes. Oxford University Press (2001)

    Google Scholar 

Download references

Acknowledgments

The authors acknowledge the Doctoral Training Centre in Complex Systems Simulation at the University of Southampton, in the completion of this work. MS was supported by an EPSRC grant (EP/G03690X/1).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Massimo Stella .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Stella, M., Brede, M. (2016). Investigating the Phonetic Organisation of the English Language via Phonological Networks, Percolation and Markov Models. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_19

Download citation

Publish with us

Policies and ethics