Automatic documents classification

Automatic document classification is of paramount importance to knowledge management in the information age. Document classification poses many challenges for learning systems since the feature vector used to represent a document must capture some of the complex semantics of natural language. In this paper, we design an automatic document classification system. We investigate the different parameters and design decisions that affect the building of automatic classifiers. The system creates an item vector for each document retrieved and assigns weights for each item. The vectors are selected using combined techniques from stemmer algorithm and natural language processing. Several weighting schema have been used. Documents are classified using neural network (NN). We investigate different cases applied to the NN classifier. Cases are classified according to weighting schema, effect of weighting words in the title, and the number of inputs to the classifier. Analyzing the performance of the classifier according to different cases is illustrated.