Metaheuristics for Tuning Model Parameters in Two Natural Language Processing Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7557)


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.


Classification Parameters Tuning Metaheuristics 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of InformaticsWrocław University of TechnologyWrocławPoland
  2. 2.Department of System EngineeringTU DresdenDresdenGermany

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