Neural Text Categorizer for Exclusive Text Categorization

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.

A survey on text mining in social networks

“…ANN can be used for the logical management of text available on Web. Jo proposed a new neural network architecture for text categorization with document presentation called Neural Text Categorizer (NTC) (Jo, 2010). NTC comprises of three layers: (a) input layer, (b) output layer, and (c) learning layer.…”

“…Owing to their tremendous ability to extract meaningful information from a huge set of data, neurons have been configured for specific application areas, such as pattern recognition, FE, and noise reduction. In the neural network, connection between two neurons determines the influence of one neuron on another, while the weight on the connection determines the strength of the influence between the two neurons (Jo, 2010).…”

“…Different types of supervised learning ANNs include: (a) back propagation and (b) modified back propagation neural networks (Luger, 2008). Major application areas of supervised learning are pattern recognition and text classification (Jo, 2010;Kolodziej et al, 2012). In unsupervised learning (clustering), the neural network tends to perform clustering by adjusting the weights based on similar inputs and distributing the task among interconnected processing elements (Luger, 2008).…”

“…Artificial neural networks (ANN) are parallel distributed processing systems specifically inspired by the biological neural systems (Jo, 2010).The network comprises of a large number of highly interconnected processing elements (neurons) working together to solve any specific problem. Owing to their tremendous ability to extract meaningful information from a huge set of data, neurons have been configured for specific application areas, such as pattern recognition, FE, and noise reduction.…”

“…In the scientific literature (Yin et al, 2007;Tekiner et al, 2009;Jo, 2010;Ringel et al, 2010), various text mining techniques are suggested to discover textual patterns from online sources. In Baharum et al (2010), the authors restrict the analysis to techniques that are specifically associated with text document classification.…”