Neural Text Categorizer for Exclusive Text Categorization

Taeho Jo
Volume: 4, No: 2, Page: 77 ~ 86, Year: 2008
10.3745/JIPS.2008.4.2.077
Keywords: Disk Neural Text Categorizer, Text Categorization, NewsPage.com
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Abstract
This research proposes a new neural network for text categorization which uses alternative representations of documents to numerical vectors. Since the proposed neural network is intended originally only for text categorization, it is called NTC (Neural Text Categorizer) in this research. Numerical vectors representing documents for tasks of text mining have inherently two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of text categorization is degraded. Even if SVM (Support Vector Machine) is tolerable to huge dimensionality, it is not so to the second problem. The goal of this research is to address the two problems at same time by proposing a new representation of documents and a new neural network using the representation for its input vector.

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Cite this article
IEEE Style
Taeho Jo, "Neural Text Categorizer for Exclusive Text Categorization," Journal of Information Processing Systems, vol. 4, no. 2, pp. 77~86, 2008. DOI: 10.3745/JIPS.2008.4.2.077.

ACM Style
Taeho Jo, "Neural Text Categorizer for Exclusive Text Categorization," Journal of Information Processing Systems, 4, 2, (2008), 77~86. DOI: 10.3745/JIPS.2008.4.2.077.