Text Classification Using Semantic Networks - Kulev, Igor; Janevska, Elizabeta; Jovanovik, Milos; Stojanov, Riste; Trajanov, Dimitar (Institute of Informatics, Skopje, Macedonia, 2011)
In the age of information overflow, we face with the challenge of categorizing the digital information we come across on a daily basis, in order to apply different operations and priorities to different types of information and to manage to use it in a more efficient manner. This issue introduces the challenge of automatic text classification. The problem of text classification can be defined as assigning one or more categories to a certain text, based on its contents. There are many different approaches for solving this problem: one of the solutions is the use of latent semantic analysis (LSA), statistical text analysis, etc. This paper introduces an algorithm for text classification with the use of semantic networks. In this paper we present a method for knowledge representation needed for this type of text analysis. We also show how to create this knowledge representation and how to use it to assign one or more categories to a given text.