Optimizing genetic algorithm in feature selection for named entity recognition

This paper proposes some strategies to reduce the running time of genetic algorithms used in a feature selection task for the problem of named entity recognition. They include: (i) reduction of population size during the evolution process of the genetic algorithm; (ii) parallelization of the fitness computation; and (iii) use of progressive sampling for calculating the optimal sample size of the training data. Maximum Entropy algorithm is then used, as a test classifier, to compute the accuracy of the named entity recognition system with the reduced feature sets identified by the genetic algorithm. Experimental results show that our improved genetic algorithm run three time faster than the standard genetic algorithm, while the accuracy of the named entity recognition system (using Maximum Entropy) on the induced feature subset does not decrease. In addition, the feature subset induced by our improved genetic algorithm is much smaller than the original feature set and has helped Maximum Entropy to achieve higher accuracy than the original one. © 2015 ACM.