CONCEPT AND TERM BASED SIMILARITY MEASURE FOR TEXT CLASSIFICATION AND CLUSTERING

The exploitation of syntactic structures and semantic background knowledge has always been an appealing subject in the context of data mining, text retrieval and information management. The usefulness of this kind of information has been shown most prominently in highly specialized tasks, such as text categorization scenarios. So far, however, additional syntactic or semantic information has been used only individually. In this paper, a new principle approach, the concept and term based similarity measure, which incorporates linguistic and semantic structures, using syntactic dependencies, and semantic background knowledge is proposed. This novel method represents the meaning of texts in a high-dimensional space of concepts derived from WordNet. A number of case studies have been included in the research to demonstrate the various aspects of this framework