Feature Subset Selection Using Genetic Algorithm for Named Entity Recognition
Mohammed Hasanuzzaman, Sriparna Saha, Asif Ekbal · Institutional Repositories DataBase (IRDB) · 2010
In this paper, genetic algorithm (GA) is utilized to search for the appropriate feature combination for constructing a maximum entropy (ME) based classifier for named entity recognition (NER). Features are encoded in the chromosomes. The ME classifier is evaluated for the 3-fold cross validation with the features, encoded in a particular chromosome, and its average F-measure value is used as the fitness value of the corresponding chromosome. The proposed technique is evaluated for determining the suitable feature combinations for NER in three resource-constrained languages, namely Bengali, Hindi and Telugu. Evaluation results show the effectiveness of the proposed approach with the overall recall, precision and F-measure values of 71.27%, 83.95% and 77.09%, respectively for Bengali, 74.72%, 87.15% and 80.46%, respectively for Hindi and 60.91%, 94.15% and 73.97%, respectively for Telugu.