Modelling (sub)string-length based constraints through a grammatical inference method
Proc. of the NATO Advanced Study Institute on Pattern recognition theory and applications
Part-of-Speech Tagging with Evolutionary Algorithms
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
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
Fast statistical parsing of noun phrases for document indexing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
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
Unsupervised query segmentation using generative language models and wikipedia
Proceedings of the 17th international conference on World Wide Web
Detecting phishing e-mails by heterogeneous classification
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
An integrated approach to filtering phishing e-mails
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
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This paper presents a new model for flexible noun phrase detection, which is able to recognize noun phrases similar enough to the ones given by the inferred noun phrase grammar. To allow this flexibility, we use a very accurate set of probabilities for the transitions between the part-of-speech tag sequence which defines a noun phrase. These accurate probabilities 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 compare the results with other systems, what has revealed a clear improvement of the performance.