In this paper a methodology for feature selection in named entity recognition is
proposed. Unlike traditional named entity recognition approaches which mainly
consider accuracy improvement as the sole objective, the innovation here is manifested
in the use of a multiobjective genetic algorithm which is employed for feature
selection basing on various aspects including error rate reduction and time taken
for evaluation, and also demonstrating the use of Pareto optimization. The proposed
method is evaluated in the context of named entity recognition, using three different
data sets and a K-nearest Neighbour machine learning algorithm. Comprehensive
experiments demonstrate the feasibility of the methodology.