Neural Network for Text Classification Based on Singular Value Decomposition

This paper proposed new text classification models based on artificial neural networks and Singular Value Decomposition (SVD). The neural networks are trained by Multi-Output Perceptron Learning algorithm (MOPL) and Back-Propagation Neural Network (BPNN). Most classic classification systems represent the contents of documents with a set of index terms, it has been known as vector space model (VSM). However, this method need a high dimensional space to represent the documents, and it dose not take into account the semantic relationship between terms, which could lead to poor classification performance. In this paper, we introduce singular value decomposition to our systems. SVD was used to learn and represent relations among very large numbers of words and very large numbers of natural text passages in which they occurred. It could not only greatly reduce the dimensional but also discover the important associative relationships between terms. It also helps to accelerate the training speed and improve the classification accuracy. We test our classification systems on the standard Reuter-21578 collection. Experimental evaluations show that the systems training with SVD are much fast then the original systems with VSM, and also achieve better classification results.