In this paper we study the inuence 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.
Geographical entities often appears in very different forms in text collections, such as when a foreign name is used instead of the English one, or when the citation of some region or place omits the name of a larger geographical entity containing them. This is a known problem in the field of Information Retrieval. The use of an ontology like WordNet can help in addressing this issue. In this paper we propose an automatic method to expand the geographical terms in queries by using the WordNet ontology and another method that expands the terms during the indexing phase. The proposed methods exploits the synonymy, meronymy and holonymy relationships provided by WordNet, together with some information extracted from the gloss.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.