A parametric methodology for text classification

Abstract

Finding the correct category (class) a new unclassified document belongs to is an interesting and difficult problem, with a wide range of applications. Our methodology for narrative text classification is based on two techniques: we calculate the distance (similarity) between the new unclassified document and all the pre-classified documents of each class and also calculate the similarity of the new document to the ‘average class document’ of each class. In both cases we use key phrases (text phrases or key terms) as the distinctive features of our text classification methodology and eventually the proposed text classification method is based on the automatic extraction of an authority list of key phrases that is appropriate for discriminating between different classes. In this paper, we apply this methodology in handling Greek text and we present the key concepts, the algorithms, and some critical decisions. A number of parameters of the mining algorithm are also fine tuned. The actual text classification system, the adopted (embedded) ideas and the alternative values of parameters are evaluated using two training sets (test collections).