On the classification of text documents taking into account their structural features - Gulin, V. V.; Frolov, A. B. (Journal of computer & systems sciences international, 2016)
A modification of the conventional bag of words model that can take into account the structural features of text documents in their classification (categorization) using machine learning techniques is studied. It is proposed to describe these features by relations on the set of certain lexemes and use the relation names, along with the lexeme names, as features. This is a distinction from the conventional model in which only unary relations are used. The effectiveness of the proposed machine learning techniques is analyzed using computer experiments on the class of the Reuters-21578 collection with eight known classifiers. It is shown that it is reasonable to apply the proposed models to classify documents using simple classifiers.