Evaluation of Semantic Similarity Using Vector Space Model Based on Textual Corpus

Abstract

In this work, we have created a semantic similarity calculation system between text documents to contribute to their semantic clustering. Indeed, semantic clustering of documents is a promising field of research, since it guarantees a quick and targeted access to information. The aim of document clustering is to put together similar documents. We used the algebraic model VSM (Vector Space Model) [2] to represent text documents and the WordNet [1] lexical database, in that it groups words together based on their meanings. In this paper, we will present an overview of the static and semantic methods for calculating the similarity measure and the appropriateness of these methods. As our research is focusing on the treatment of text documents on e-learning systems. We worked on a corpus of a set of text documents from the computer science textbook for high school students in Morocco. To evaluate our system, an experiment has been conducted among students who produced text documents. Experimental evaluations using WordNet prove that the system presented in this work improves the accuracy of semantic similarity between the text documents.