Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Information Processing and Management: an International Journal
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
A hybrid approach for named entity and sub-type tagging
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Named Entity recognition without gazetteers
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
ACM Transactions on Asian Language Information Processing (TALIP)
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Genetic algorithms for feature relevance assignment in memory-based language processing
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
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
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity discovery using comparable news articles
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Natural language tagging with genetic algorithms
Information Processing Letters
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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
How evolutionary algorithms are applied to statistical natural language processing
Artificial Intelligence Review
Chinese named entity recognition with cascaded hybrid model
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Voted NER system using appropriate unlabeled data
NEWS '09 Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Data & Knowledge Engineering
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In this paper, we propose a classifier ensemble technique based on genetic algorithm (GA) for named entity recognition (NER). We assume that the classifiers based on different feature representations can be effectively combined together using GA to achieve better performance. The proposed approach is also able to find the appropriate ensemble approach, i.e. either majority voting or weighted voting. Maximum entropy (ME) model is used as a base to generate a number of different classifiers depending upon the various representations of the available features. The proposed approach is evaluated for three leading Indian languages, namely Bengali, Hindi and Telugu. Evaluation results yield the recall, precision and F-measure values of 88.12, 93.99 and 90.96%, respectively for Bengali, 80.26, 92.70 and 86.03%, respectively for Hindi and 74.79, 85.38 and 79.73%, respectively for Telugu. We also evaluate the proposed approach with the CoNLL-2003 benchmark English datasets and it shows the recall, precision and F-measure values of 83.05, 85.52 and 84.27%, respectively. It is observed that the GA based ensemble attains the performance which is superior to all the individual classifiers as well as two conventional baseline ensembles for all the languages.