@article{125,
  author = {Fouzi Harrag, Eyas Al-Qawasmah},
  title = {Improving Arabic Text Categorization Using Neural Network with SVD},
  journal = {Journal of Digital Information Management},
  year = {2010},
  volume = {8},
  number = {2},
  doi = {},
  url = {http://www.dline.info/fpaper/jdim/v8i4/2.pdf},
  abstract = {In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifi able and faster for the convergence of the training process used in the NN model. To test the effectiveness of the proposed model, experiments were conducted using an in-house collected Arabic corpus for text categorization. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by precision, recall and F-measure. Experimental result shows that the ANN model using SVD is better than the basic ANN on Arabic text classifi cation.},
}