The vocabulary problem in human-system communication
Communications of the ACM
Modelling (sub)string-length based constraints through a grammatical inference method
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Part-of-Speech Tagging with Evolutionary Algorithms
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A memory-based approach to learning shallow natural language patterns
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Error-driven pruning of Treebank grammars for base noun phrase identification
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Surface grammatical analysis for the extraction of terminological noun phrases
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 3
Tagging and chunking with bigrams
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Detecting novel compounds: the role of distributional evidence
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
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
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - 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
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Voting between multiple data representations for text chunking
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Symbiosis of evolutionary techniques and statistical natural language processing
IEEE Transactions on Evolutionary Computation
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
IEEE Transactions on Information Theory
Evolutionary Shallow Natural Language Parsing
Computational Intelligence
Semi-supervised constituent grammar induction based on text chunking information
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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We present a new model for detection of noun phrases in unrestricted text, whose most outstanding feature is its flexibility: the system is able to recognize noun phrases similar enough to the ones given by the inferred noun phrase grammar. The system provides a probabilistic finite-state automaton able to recognize the part-of-speech tag sequences which define a noun phrase. The recognition flexibility is possible by using a very accurate set of rankings for the FSA transitions. These accurate rankings are obtained by means of an evolutionary algorithm, which works with both, positive and negative examples of the language, thus improving the system coverage while maintaining its precision. We have tested the system on different corpora and evaluated different aspects of the system performance. We have also investigated other ways of improving the performance such as the application of certain filters in the training sets. The comparison of our results with other systems has revealed a considerable performance improvement.