An introduction to genetic algorithms
An introduction to genetic algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Principle-Based Parsing: Computation and Psycholinguistics
Principle-Based Parsing: Computation and Psycholinguistics
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
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
Chunking with maximum entropy models
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Exploring evidence for shallow parsing
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Evolutionary Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Natural language tagging with genetic algorithms
Information Processing Letters
Highly accurate error-driven method for noun phrase detection
Pattern Recognition Letters
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A semantically guided and domain-independent evolutionary model for knowledge discovery from texts
IEEE Transactions on Evolutionary Computation
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Identifying syntactical information from natural-language texts requires the use of sophisticated parsing techniques mainly based on statistical and machine-learning methods. However, due to complexity and efficiency issues many intensive natural-language processing applications using full syntactic analysis methods may not be effective when processing large amounts of natural-language texts. These tasks can adequately be performed by identifying partial syntactical information through shallow parsing (or chunking) techniques. In this work, a new approach to natural-language chunking using an evolutionary model is proposed. It uses previously captured training information to guide the evolution of the model. In addition, a multiobjective optimization strategy is used to produce unique quality values for objective functions involving the internal and the external quality of chunking. Experiments and the main results obtained using the model and state-of-the-art approaches are discussed. © 2012 Wiley Periodicals, Inc.