Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ACM Transactions on Asian Language Information Processing (TALIP)
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
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Multiobjective optimization approach for named entity recognition
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
ACM Transactions on Asian Language Information Processing (TALIP)
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In this paper, we report the search capability of genetic algorithm (GA) to construct a weighted vote based classifier ensemble for Named Entity Recognition (NER). Our underlying assumption is that the reliability of predictions of each classifier differs among the various named entity (NE) classes. Weights of voting should be high for the NE classes for which the classifier is most reliable and low for the NE classes for which the classifier is not at all reliable. Here, an attempt is made to quantify the amount of voting for each class in each classifier using GA. We use Maximum Entropy (ME) framework to build a number of classifiers depending upon the various representations of a set of features, language independent in nature. The proposed technique is evaluated with two resource-constrained languages, namely Bengali and Hindi. Evaluation results yield the recall, precision and F-measure values of 73.81%, 84.92% and 78.98%, respectively for Bengali and 65.12%, 82.03% and 72.60%, respectively for Hindi. Results also show that the proposed weighted vote based classifier ensemble identified by GA outperforms all the individual classifiers and three conventional baseline ensemble techniques for both the languages.