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Metaheuristics for Tuning Model Parameters in Two Natural Language Processing Applications

  • Łukasz Kłyk
  • Paweł B. Myszkowski
  • Bartosz Broda
  • Maciej Piasecki
  • David Urbansky
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7557)

Abstract

Choosing model parameters is an important issue for solving real word problems. Wrong parameter values result in low performance of employed model. Usually, parameters are chosen manual, but one can employ metaheuristics for searching the parameter space in more systematic and automated way. In this paper we test a few optimisation methods such as Evolutionary Algorithms, Tabu Search, Hill Climbing and Simulated Annealing for setting parameters of models in two problems in the domain of Natural Language Processing. Metaheuristics used significantly improve performance in comparison to the default parameter selected manually by domain experts.

Keywords

Classification Parameters Tuning Metaheuristics 

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References

  1. 1.
    Kohavi, R.: Automatic parameter selection by minimizing estimated error. In: Proc. of 12th Twelfth ICML 1995, p. 304 (1995)Google Scholar
  2. 2.
    Wang, J., Downs, T.: Tuning pattern classifier parameters using a ga with an application in mobile robotics. In: Cong. CEC 2003, pp. 581–586. IEEE (2003)Google Scholar
  3. 3.
    Michalewicz, Z., Fogel, D.B.: How to solve it: modern heur. Springer (2004)Google Scholar
  4. 4.
    Chunhong, Z., Licheng, J.: Automatic parameters selection for svm based on ga. In: 5th Word Congr. WCICA 2004, vol. 2, pp. 1869–1872. IEEE (2004)Google Scholar
  5. 5.
    Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for svm. Machine Learning 46(1), 131–159 (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Müssel, C., Lausser, L., Maucher, M., Kestler, H.: Multi-objective parameter selection for classifiers. Journal of Statistical Software 46(i05) (2008)Google Scholar
  7. 7.
    Urbansky, D., Muthmann, K., Katz, P., Reichert, S.: Tud palladian overv. (2011)Google Scholar
  8. 8.
    Fellbaum, C. (ed.): WordNet — An Electr. Lexical Database. The MIT Press (1998)Google Scholar
  9. 9.
    Broda, B., Kurc, R., Piasecki, M., Ramocki, R.: Evaluation Method for Automated Wordnet Expansion. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds.) SIIS 2011. LNCS, vol. 7053, pp. 293–306. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Łukasz Kłyk
    • 1
  • Paweł B. Myszkowski
    • 1
  • Bartosz Broda
    • 1
  • Maciej Piasecki
    • 1
  • David Urbansky
    • 2
  1. 1.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  2. 2.Department of System EngineeringTU DresdenDresdenGermany

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