Back propagation is sensitive to initial conditions
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
Feature selection in SVM text categorization
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 decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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)
The effect of named entities on effectiveness in cross-language information retrieval evaluation
Proceedings of the 2005 ACM symposium on Applied computing
Unsupervised learning of generalized names
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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 with a maximum entropy approach
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
Named entity recognition with character-level models
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
A stacked, voted, stacked model for named entity recognition
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
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
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
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
Phrase clustering for discriminative learning
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
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
Data & Knowledge Engineering
| Hi-index | 0.00 |
In this article, 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. Thus, it is necessary to quantify the amount of voting of a particular classifier for a particular output class. Here, an attempt is made to determine the appropriate weights of voting for each class in each classifier using GA. The proposed technique is evaluated for four leading Indian languages, namely Bengali, Hindi, Telugu, and Oriya, which are all resource-poor in nature. Evaluation results yield the recall, precision and F-measure values of 92.08%, 92.22%, and 92.15%, respectively for Bengali; 96.07%, 88.63%, and 92.20%, respectively for Hindi; 78.82%, 91.26%, and 84.59%, respectively for Telugu; and 88.56%, 89.98%, and 89.26%, respectively for Oriya. Finally, we evaluate our proposed approach with the benchmark dataset of CoNLL-2003 shared task that yields the overall recall, precision, and F-measure values of 88.72%, 88.64%, and 88.68%, respectively. Results also show that the vote based classifier ensemble identified by the GA-based approach outperforms all the individual classifiers, three conventional baseline ensembles, and some other existing ensemble techniques. In a part of the article, we formulate the problem of feature selection in any classifier under the single objective optimization framework and show that our proposed classifier ensemble attains superior performance to it.