Text Categorization and Information Retrieval Using WordNet Senses

In this paper we study the influence of semantics in the Text Categorization (TC) and Information Retrieval (IR) tasks. The K Nearest Neighbours (K-NN) method was used to perform the text categorization. The experimental results were obtained taking into account for a relevant term of a document its corresponding WordNet synset. For the IR task, three techniques were investigated: the direct use of a weighted matrix, the Singular Value Decomposition (SVD) technique in the Latent Semantic Indexing (LSI) model, and the bisecting spherical k-means clustering technique. The experimental results we obtained taking into account the semantics of the documents, allowed for an improvement of the performance for the text categorization whereas they were not so promising for the IR task.