Categorization of text documents taking into account some structural features

Abstract

This paper reviews the possibility of upgrading the conventional “bag-of-words” model to reflect the structural features of text documents and take them into account in the process of categorization by means of machine learning theory methods. It is suggested to use these features to characterize the relationships within a set of tokens. It is also proposed to use the names of such relationships as features, along with the names of tokens. The proposed models differ from the traditional approach, which only reflects unary relations. The efficiency of the upgraded methods of machine learning is tested by means of computer experiments run for the Reuters-21578 set classes by using eight common classifiers. The relevance of applying such a modernized approach to categorize text documents with the help of simple classifiers is demonstrated.