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PNEPs, NEPs for context free parsing: Application to natural language processing

Author
Ortega de la Puente, Alfonsountranslated; Rosal García, Emilio del; Pérez Marín, Diana; Mercaş, Robert; Perekrestenko, Alexander; Alfonseca, Manuel
Entity
UAM. Departamento de Ingeniería Informática
Publisher
Springer Berlin Heidelberg
Date
2009
Citation
Bio-Inspired Systems: Computational and Ambient Intelligence: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, Part I. Lecture Notes in Computer Science, Volumen 5517. Springer, 2009. 472-479
ISSN
0302-9743 (print); 1611-3349 (online)
ISBN
978-3-642-02477-1 (print); 978-3-642-02478-8 (online)
DOI
10.1007/978-3-642-02478-8_59
Funded by
This work was partially supported by MEC, project TIN2008-02081/TIN and by DGUI CAM/UAM, project CCG08-UAM/TIC-4425.
Editor's Version
http://dx.doi.org/978-3-642-02478-8
Subjects
Computational Biology; Bioinformatics; Pattern Recognition; Artificial Intelligence; Data Mining; Knowledge Discovery; Informática
URI
http://hdl.handle.net/10486/664094
Note
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02478-8_59
Proceedings of 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Salamanca, Spain.
Rights
© Springer-Verlag Berlin Heidelberg 2009

Abstract

This work tests the suitability of NEPs to parse languages. We propose PNEP, a simple extension to NEP, and a procedure to translate a grammar into a PNEP that recognizes the same language. These parsers based on NEPs do not impose any additional constrain to the structure of the grammar, which can contain all kinds of recursive, lambda or ambiguous rules. This flexibility makes this procedure specially suited for Natural Languge Processing (NLP). In a first proof with a simplified English grammar, we got a performance (a linear time complexity) similar to that of the most popular syntactic parsers in the NLP area (Early and its derivatives). All the possible derivations for ambiguous grammars were generated
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Google™ Scholar:Ortega de la Puente, Alfonso - Rosal García, Emilio del - Pérez Marín, Diana - Mercaş, Robert - Perekrestenko, Alexander - Alfonseca, Manuel

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