Abstract
The feature selection is an important part in automatic classification. In this paper, we use the HowNet to extract the concept attributes, and propose CHI-MCOR method to build a feature set. This method not only selects the highly occurring words, but also selects the word whose occurrence frequency is middle or low occurring words that are important for text classification. The combined method is much better than any one of the weight methods. Then we use the Self-Organizing Map (SOM) to realize automatic text clustering. The experiment result shows that if we can extract the sememes properly, we can not only reduce the feature dimension but also improve the classification precise. SOM can be used in text clustering in large scales and the clustering results are good when the concept feature is selected.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, P., Jin, Z., Tan, L.H.: Neural Representations of Nouns and Verbs in Chinese: an fMRI Study. NeuroImage 21, 1533–1541 (2004)
Jiang, M., Cai, H., Zhang, B.: Self-organizing Map Analysis Consistent with Neuroimaging for Chinese Noun, Verb and Class-ambiguous Word. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3498, pp. 971–976. Springer, Heidelberg (2005)
Liao, S., Jiang, M.: An Improved Method of Feature Selection Based on Concept Attributes in Text Classification. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 1140–1149. Springer, Heidelberg (2005)
Davies, D., Bouldin, D.: A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence - I 2, 224–227 (1979)
Kohonen, T.: The Self-organnized Map. Proceedings of the IEEE 78, 1464–1480 (1990)
Vesanto, J., Alhoniemi, J.: Clustering of the Self-organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Wang, L., Jiang, M., Lu, Y., et al.: Self-organizing Map Clustering Analysis for Molecular Data. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1250–1255. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Jiang, M., Liao, S., Deng, B., Zong, C., Lu, Y. (2006). Concept Features Extraction and Text Clustering Analysis of Neural Networks Based on Cognitive Mechanism. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_23
Download citation
DOI: https://doi.org/10.1007/11816157_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
eBook Packages: Computer ScienceComputer Science (R0)