Automated Text Classification in the DMOZ Hierarchy

The growth in the availability of on-line digital text documents has prompted considerable interest in Information Retrieval and Text Classification. Automation of the management of this wealth of textual data is becoming an increasingly important endeavor as the rate of new material continues to grow at its substantial rate. The open directory project (ODP) also known as DMOZ is an on-line service which provides a searchable and browsable hierarchically organised directory to facilitate access to the Internets’ resources. This resource is considerably useful for the construction of intelligent systems for on-line content management. In this report the utility of the publicly available Open Directory Project data for the classification of World Wide Web (WWW) text documents is investigated. The resource is sampled and a range of algorithms are applied to the task namely, Support Vector Machines (SVM), Multi-class Rocchio (Centroid), k-Nearest Neighbour, and Naı̈ve Bayes (NB). The theoretical and implementation details of the four text classification systems are discussed. Results from the tuning and performance of these algorithms are analysed and compared with published results. Related work from the areas of both text classification and classification in general is surveyed. Some of the unique issues of large scale multi-class text classification are identified and analysed.