Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
ACM Transactions on Asian Language Information Processing (TALIP)
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, we report a multiobjective optimization (MOO) based classifier ensemble technique to solve the problem of Named Entity Recognition (NER). Our underlying assumption is that rather than searching for the best feature set for a particular classifier, ensembling of several classifiers which are trained using different feature representations could be a more fruitful approach. But, it is very crucial to select the appropriate classifiers that can participate in final ensembling. Here, we propose a new technique for classifier ensembling based on MOO that can simultaneously optimize several different classification measures. Maximum Entropy (ME) framework is used to generate a number of classifiers by considering the various combinations of the available features. The proposed technique is evaluated for two resource constrained languages, namely Bengali and Hindi. Evaluation results yield the recall, precision and F-measure values of 72.34%, 84.94% and 78.13%, respectively for Bengali, and 64.93%, 83.29% and 72.97%, respectively for Hindi. Experiments also show that the classifier ensemble identified by the proposed multiobjective based approach outperforms all the individual classifiers, two different baseline ensembles and a classifier ensemble identified by the single objective genetic algorithm (GA) based approach.