Hybrid Feature Selection Approach for Arabic Named Entity Recognition

Abstract

Named Entity Recognition (NER) task has drawn a great attention in the research field in the last decade, as it played an important role in the Natural Language Processing (NLP) applications; In this paper, we investigate the effectiveness of a hybrid feature subset selection approach for Arabic Named Entity Recognition (NER) which is presented using filtering approach and optimized Genetic algorithm. Genetic algorithm is utilized through parallelization of the fitness computation in order to reduce the computation time to search out the most appropriate and informative combination of features for classification. Support Vector Machine (SVM) is used as the machine learning based classifier to evaluate the accuracy of the Arabic NER through the proposed approach. ANER is the dataset used in our experiments which is presented by both language independent and language specific features in Arabic NER; Experimental results show the effectiveness of the feature subsets obtained by the proposed hybrid approach which are smaller and effective than the original feature set that leads to a considerable increase in the classification accuracy.