Differential Evolution based Feature Selection and Classifier Ensemble for Named Entity Recognition

摘要

In this paper, we propose a differential evolution (DE) based two-stage evolutionary approach for named entity recognition (NER). The first stage concerns with the problem of relevant feature selection for NER within the frameworks of two popular machine learning algorithms, namely Conditional Random Field (CRF) and Support Vector Machine (SVM). The solutions of the final best population provides different diverse set of classifiers; some are effective with respect to recall whereas some are effective with respect to precision. In the second stage we propose a novel technique for classifier ensemble for combining these classifiers. The approach is very general and can be applied for any classification problem. Currently we evaluate the proposed algorithm for NER in three popular Indian languages, namely Bengali, Hindi and Telugu. In order to maintain the domain-independence property the features are selected and developed mostly without using any deep domain knowledge and/or language dependent resources. Experimental results show that the proposed two stage technique attains the final F-measure values of 88.89%, 88.09% and 76.63% for Bengali, Hindi and Telugu, respectively. The key contributions of this work are two-fold, viz. (ⅰ). proposal of differential evolution (DE) based feature selection and classifier ensemble methods that can be applied to any classification problem; and (ⅱ). scope of the development of language independent NER systems in a resource-poor scenario.