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
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
Improving machine translation quality with automatic named entity recognition
EAMT '03 Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In this paper, we propose a multiobjective optimization (MOO) based technique to determine the appropriate weight of voting for each class in each classifier for Named Entity Recognition (NER). Our underlying assumption is that reliability of predictions of each classifier differs among the various named entity (NE) classes. Thus, it is necessary to quantify the amount of voting for each class in a particular classifier. We use Maximum Entropy (ME) as the base to generate a number of classifiers depending upon the various feature representations. The proposed algorithm is evaluated for a resource-constrained language like Bengali that yield the overall recall, precision and F-measure values of 79.98%, 82.24% and 81.10%, respectively. Experiments also show that the classifier ensemble identified by the proposed multiobjective based technique outperforms all the individual classifiers, three different conventional baseline ensembles and an existing single objective optimization based approach.