Abstract
The paper describes the application of artificial neural networks for corpus analysis which consists of intelligent mechanisms of analysis and recognition of word clusters and their meaning. The task of analyzing a corpus of academic articles was resolved with probabilistic neural networks. A review of selected issues is carried out with regards to computational approaches to language modeling as well as statistical patterns of language. The paper features recognition algorithms of word clusters of similar meanings but different lexico-grammatical patterns from the established corpus using four-layer neural networks. The paper also presents experimental results of word cluster recognition in the context of phrase meaning analysis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Parzen, E.: On estimation of a probability density function and mode. The Annals of Mathematical Statistics 33(3), 1065–1076 (1962)
Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)
Specht, D.F.: Enhancements to probabilistic neural networks. In: Proc. IEEE International Joint Conference on Neural Networks, Baltimore, USA, vol. 1, pp. 761–768 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stuart, K.D., Majewski, M., Trelis, A.B. (2010). Selected Problems of Intelligent Corpus Analysis through Probabilistic Neural Networks. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)