This paper addresses the problem of learning to classify texts by exploiting information derived from both training and testing sets. To accomplish this, clustering is used as a complementary step to text classification, and is applied not only to the training set but also to the testing set. This approach allows us to estimate the location of the testing examples and the structure of the whole...

We examine the impact on classification effectiveness of semantic differences in categories. Specifically, we measure broadness and narrowness of categories in terms of their distance to the root of a hierarchically organized thesaurus. Using categories of four different levels degrees of broadness, we show that classifying documents into narrow categories gives better scores than classifying t...

We explore scalability issues of the text classification problem where using (multi)labeled training documents we try to build classifiers that assign documents into classes permitting classification in multiple classes. A new class of classification problems, called ‘scalable’ is introduced that models many problems from the area of Web mining. The property of scalability is defined as the abi...