Discriminating word senses with tourist walks in complex networks

  • Thiago C. Silva
  • Diego R. Amancio
Regular Article


Patterns of topological arrangement are widely used for both animal and human brains in the learning process. Nevertheless, automatic learning techniques frequently overlook these patterns. In this paper, we apply a learning technique based on the structural organization of the data in the attribute space to the problem of discriminating the senses of 10 polysemous words. Using two types of characterization of meanings, namely semantical and topological approaches, we have observed significative accuracy rates in identifying the suitable meanings in both techniques. Most importantly, we have found that the characterization based on the deterministic tourist walk improves the disambiguation process when one compares with the discrimination achieved with traditional complex networks measurements such as assortativity and clustering coefficient. To our knowledge, this is the first time that such deterministic walk has been applied to such a kind of problem. Therefore, our finding suggests that the tourist walk characterization may be useful in other related applications.


Statistical and Nonlinear Physics 

Supplementary material


  1. 1.
    C.D. Manning, H. Schtze, Foundations of Statistical Natural Language Processing (MIT Press, Cambridge, 1999)Google Scholar
  2. 2.
    C.E. Shannon, Bell System Technical Journal 27, 379 (1948)MathSciNetzbMATHGoogle Scholar
  3. 3.
    P. Carpena, P. Bernaola-Galvn, M. Hackenberg, A.V. Coronado, J.L. Oliver, Phys. Rev. E 79, 035102 (2009)ADSCrossRefGoogle Scholar
  4. 4.
    M. Ortuño, P. Carpena, P. Bernaola-Galván, E. Muñoz, A.M. Somoza, Europhys. Lett. 57, 759 (2009)ADSCrossRefGoogle Scholar
  5. 5.
    C.M. Alis, M.T. Lim, Eur. Phys. J. B 85, 397 (2012)ADSCrossRefGoogle Scholar
  6. 6.
    X. Castell, A. Baronchelli, V. Loreto, Eur. Phys. J. B 71, 557 (2009)ADSCrossRefGoogle Scholar
  7. 7.
    J.P. Herrera, P.A. Pury, Eur. Phys. J. B 63, 135 (2008)ADSCrossRefGoogle Scholar
  8. 8.
    L.F. Costa, F.A. Rodrigues, G. Travieso, P.R. Villas Boas, Adv. Phys. 56, 167 (2007)ADSCrossRefGoogle Scholar
  9. 9.
    D.R. Amancio, O.N. Oliveira Jr., L.F. Costa, Physica A 391, 4406 (2012)ADSCrossRefGoogle Scholar
  10. 10.
    A.P. Masucci, G.J. Rodgers, Phys. Rev. E 74, 026102 (2006)ADSCrossRefGoogle Scholar
  11. 11.
    D.R. Amancio, O.N. Oliveira Jr., L.F. Costa, New J. Phys. 14, 043029 (2012)ADSCrossRefGoogle Scholar
  12. 12.
    R. Ferrer i Cancho, R.V. Sole, Proc. Natl. Acad. Sci. USA 100, 788 (2003)MathSciNetADSzbMATHCrossRefGoogle Scholar
  13. 13.
    R. Navigli, ACM Comput. Surv. 41, 2 (2009)CrossRefGoogle Scholar
  14. 14.
    M. Lesk, in Proceedings of the 1986 SIGDOC Conference, Association for Computing Machinery, 1986, pp. 24–26Google Scholar
  15. 15.
    P.M. Roget, Rogets International Thesaurus, 1st edn. (Cromwell, New York, 1911)Google Scholar
  16. 16.
    G.A. Miller, Communications of the ACM 38, 11 (1995)CrossRefGoogle Scholar
  17. 17.
    Oxford Dictionary of English, edited by C. Soanes, A. Stevenson (Oxford University Press, 2003)Google Scholar
  18. 18.
    A. Montoyo, M. Palomar, G. Rigau, A. Suarez, J. Artif. Intell. Res. 23, 299 (2005)zbMATHGoogle Scholar
  19. 19.
    L. Rokach, O.Z. Maimon, Data mining with decision trees: theory and applications (World Scientific Pub. Co. Inc., Singapore, 2008)Google Scholar
  20. 20.
    S.O. Haykin, Neural networks and learning machines (Prentice Hall, Upper Saddle River, 2008)Google Scholar
  21. 21.
    D.R. Amancio, O.N. Oliveira Jr., L.F. Costa, Europhys. Lett. 98, 18002 (2012)ADSCrossRefGoogle Scholar
  22. 22.
    T.C. Silva, L. Zhao, IEEE Transactions on Neural Networks and Learning Systems 23, 6 (2012)Google Scholar
  23. 23.
    G.F. Lima, A.S. Martinez, O. Kinouchi, Phys. Rev. Lett. 87, 010603 (2001)ADSCrossRefGoogle Scholar
  24. 24.
    R.O. Duda, P.E. Hart, D.G. Stork, Pattern classification (Wiley-Interscience, 2000)Google Scholar
  25. 25.
    J.R. Quinlan, C4.5: Programs for machine learning (Morgan Kaufmann, San Mateo, 1992)Google Scholar
  26. 26.
    T.C. Silva, L. Zhao, IEEE Transactions on Neural Networks and Learning Systems 23, 3 (2012)Google Scholar
  27. 27.
    T.C. Silva, L. Zhao, (2013)
  28. 28.
    C. Cortes, V.N. Vapnik, Machine Learning 20, 273 (1995)zbMATHGoogle Scholar
  29. 29.
    R. Kohavi, Proceedings of the 14th International Joint Conference on Artificial Intelligence 2, 12 (1995)Google Scholar
  30. 30.
    J.R. Bertini, A.A. Lopes, L. Zhao, J. Braz. Comput. Soc. 18, 1 (2012)MathSciNetCrossRefGoogle Scholar
  31. 31.
    I.N. Silva, R.A. Flauzino, Lect. Notes Comput. Sci. 5768, 807 (2009)CrossRefGoogle Scholar
  32. 32.
    K. Faceli, A.C.P.L.F. Carvalho, S.O. Rezende, Appl. Intell. 20, 199 (2004)zbMATHCrossRefGoogle Scholar
  33. 33.
    X. Geng, Y. Wang, Europhys. Lett. 88, 38002 (2009)ADSCrossRefGoogle Scholar
  34. 34.
    D.R. Amancio, O.N. Oliveira Jr., L.F. Costa, Europhys. Lett. 99, 48002 (2012)ADSCrossRefGoogle Scholar
  35. 35.
    D.R. Amancio, O.N. Oliveira Jr., L.F. Costa, J. informetrics 6, 427 (2012)CrossRefGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Mathematics and Computer Science, University of São PauloSão CarlosBrazil
  2. 2.Institute of Physics of São Carlos, University of São PauloSão CarlosBrazil

Personalised recommendations