Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition

In this paper, we propose a classifier ensemble technique based on genetic algorithm (GA) for named entity recognition (NER). We assume that the classifiers based on different feature representations can be effectively combined together using GA to achieve better performance. The proposed approach is also able to find the appropriate ensemble approach, i.e. either majority voting or weighted voting. Maximum entropy (ME) model is used as a base to generate a number of different classifiers depending upon the various representations of the available features. The proposed approach is evaluated for three leading Indian languages, namely Bengali, Hindi and Telugu. Evaluation results yield the recall, precision and F-measure values of 88.12, 93.99 and 90.96%, respectively for Bengali, 80.26, 92.70 and 86.03%, respectively for Hindi and 74.79, 85.38 and 79.73%, respectively for Telugu. We also evaluate the proposed approach with the CoNLL-2003 benchmark English datasets and it shows the recall, precision and F-measure values of 83.05, 85.52 and 84.27%, respectively. It is observed that the GA based ensemble attains the performance which is superior to all the individual classifiers as well as two conventional baseline ensembles for all the languages.