Mark Last

Most text categorization methods are based on the vector-space model of information retrieval. One of the important advantages of this representation model is that it can be used by both instance-based and model-based classifiers for categorization. However, this popular method of document representation does not capture important structural information, such as the order and proximity of word occurrence or the location of a word within the document. It also makes no use of the mark-up information that is available from web document HTML tags. A recently developed graph-based representation of web documents can preserve the structural information. The new document model was shown to outperform the traditional vector representation, using the k-Nearest Neighbor (k-NN) classification algorithm. The problem, however, is that the eager (model-based) classifiers cannot work with this representation directly. In this chapter, three new, hybrid approaches to web document categorization are presented, built upon both graph and vector space representations, thus preserving the benefits and overcoming the limitations of each. The hybrid methods presented here are compared to vector-based models using two model-based classifiers (C4.5 decision-tree algorithm and probabilistic Naïve Bayes) and several benchmark web document collections. The results demonstrate that the hybrid methods outperform, in most cases, existing approaches in terms of classification accuracy, and in addition, achieve a significant increase in the categorization speed.